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AI Recruiting & Hiring Software - iCIMS AI
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AI Recruiting & Hiring Software
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https://www.icims.com
|
[] |
Transform hiring with AI recruiting software from iCIMS. Automate talent sourcing, ranking and engagement to build winning teams faster and smarter.
|
iCIMS AI
AI where and when you need it
AI for the modern talent acquisition team. Deliver faster, smarter and fairer talent acquisition at every step of the recruiting and hiring lifecycle with iCIMS AI recruiting solutions.
| 2022-12-01T00:00:00 |
https://www.icims.com/products/ai-recruiting-software/
|
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|
6 Tips to Use Artificial Intelligence in the Hiring Process - Dexian
|
6 Tips to Use Artificial Intelligence in the Hiring Process
|
https://dexian.com
|
[] |
AI can help expedite and automate necessary but time-consuming workflows and processes that impede the hiring process.
|
by Kip Havel, Dexian Chief Marketing Officer
Effective hiring has hinged on the ability of HR and recruiting professionals’ ability to distinguish exceptional talent from a sea of candidates. This has, at its best, been a true people process, where humans connect to make careers and corporations better. Yet, in this rapidly evolving landscape, artificial intelligence is reshaping the paradigm, streamlining processes, and automating virtually all but the most intricate facets of recruitment.
That in and of itself is not necessarily a bad thing. AI can help expedite and automate necessary but time-consuming workflows and processes that impede the hiring process. However, as organizations quickly adopt new technology, it is important modernization doesn’t come at the expense of morality or efficacy. The challenge for all of us is learning how to fuse innovation with the boundless creativity of the human spirit.
Which Processes Can You Automate?
Goldman Sachs predicts that about 25-50% of the workload of many jobs can be outright replaced by artificial intelligence. For the most part, that accounts for tasks that are easily repeatable or formulaic. Algorithms shine when they are handling high-volume processes that do not require much critical thinking or present too much novelty.
In the hiring and recruiting world, these steps are particularly ripe for artificial intelligence:
Candidate Sourcing and Screening – The manual process of proactively finding quality candidates has always been time-consuming. Skilled recruiters know how to speed up sourcing and screening, but it’s not the best use of their time, making this a perfect task for automation. Machine learning features enable HR professionals and recruiters to create automated search parameters with job titles, skills, keywords, and locations at their core. AI-powered platforms can even begin to learn lessons from the candidates who make it through every stage of selection and even stay with your organization, improving long-term hiring outcomes.
First Interviews – The pandemic familiarized people with virtual interviews and even pre-recorded video responses, but the technology is evolving even beyond those benchmarks. Automated video interviews (AVI) incorporate bots that not only collect candidates’ answers to your questions, but also data on their non-verbal communication cues, tone of voice, vocabulary, and keyword usage. If used carefully, AVI platforms can provide business leaders with some essential information. You can learn how well they grasp their subject matter and whether they know how to communicate information to their target audience (an indispensable skill for any job). Though we’re seeing more instances of AI-led interview tools that offer recommendation engines, it’s important to tread with caution: these unsupervised features can lead to unchecked biases (more on that below).
Notetaking – Jotting down thoughts, impressions, and follow-up questions during the interview is how most experienced managers and recruiters make decisions and differentiate between candidates (interviews aren’t all that’s on their minds). However, if someone is diligently typing responses verbatim or in-depth thoughts in real-time, they can lose the thread of a conversation or miss key details. This is a perfect time when artificial intelligence in the hiring process makes a difference. Tools like HireLogic can summarize the key talking points of calls and even notate any potential follow-ups. With the right integration, these algorithm-fueled notetaking programs can upload these recaps within applicant tracking systems and other hiring tools without skipping a beat.
Which Processes Still Need People?
Sometimes business leaders and innovators preoccupy themselves with whether they can use AI and don’t stop to think if they should. This technology is truly remarkable, with immense potential. Yet, it’s crucial to bear in mind that technology alone, without the enriching embrace of meaningful human interaction, can lead to disconnects that hinder us from achieving the extraordinary experiences we should all aspire to create. Here’s where we think human beings still have a valuable part to play:
Follow-up Interviews – At the end of the day, your candidates will be interacting with human beings on the job, so simply testing one-sided recorded responses does them an injustice. How a candidate interacts with members of your team or a trusted recruiter can preview their demeanor on the job. Experienced HR leaders and recruiters have more value than just gauging how “people-friendly” a candidate might be. Their extensive knowledge can shepherd job interviews, using their knowledge to ask questions that build upon what’s already been discussed as well as respectively push back or dive deeper into yellow-flag responses.
Relationship Building – Hiring should never feel like a transactional exchange. The goal is to find someone who can fit within your organization, whether that’s on a permanent or contract basis. Everyone who is pulled into your hiring funnel should feel that personal touch, even if you don’t hire them. Artificial intelligence skill cannot emulate emotional intelligence (EQ). Their natural language processing abilities can tailor messages to answer questions, but they can’t share stories or build connections the way humans can. This is where the fusion between process and people remains crucial. The best recruiters not only take the time to talk with candidates, but they check in, share stories, exchange funny videos, and do the all-too-human work of building lasting relationships.
Bias Elimination – Artificial intelligence’s hyper-focus on completing tasks is both a strength and a caution. These tools don’t stop to “think” about peripheral concerns, possible side effects, or the implications of their actions. They just act. Moreover, as machines learn in order to optimize, we must ensure the people positioned to provide that learning don’t carry troublesome biases into the process.
Modern AI algorithms will not catch biases or might even entrench those biases the way Amazon’s failed AI-recruiting tool did when they systematized preferring male over female candidates. On the other hand, experienced HR leaders and recruiters can step back, take a deep breath, and evaluate the larger circumstances, catching any partiality or discrimination before it hurts your talent funnel or organization.
How Dexian Is Leveraging AI
When it comes to every technology decision, we always ask this question: how does this measure serve our greater purpose of being in the business for good? When it comes to artificial intelligence in the hiring process, we’ve made a commitment to implementing the technology while also respecting what humans do best.
Part of our early success has been partnering with HireLogic. Their emphasis on using their own recruiting knowledge to create an intuitive experience for staffing professionals is allowing our people to effectively accelerate hiring while also building lasting bonds with leaders and tech professionals. In fact, their conversational analytics are giving our team invaluable insights that are informing candidate recommendations, highlighting new opportunities, and improving outcomes at every level.
Plus, our own growing expertise around AI and cognitive services is helping us to guide ethical and effective implementation across our business. It’s partnerships and experience like this that empowers Dexian to respect people-centric processes while enhancing workflows at the speed and scale of today’s business world. Who says you can’t have it both ways?
| 2023-12-07T00:00:00 |
2023/12/07
|
https://dexian.com/blog/artificial-intelligence-in-the-hiring-process/
|
[
{
"date": "2022/12/01",
"position": 71,
"query": "artificial intelligence hiring"
}
] |
AI in Hiring: Smarter Recruitment Strategies for 2025 - Lisa Masiello
|
AI in Hiring: Smarter Recruitment Strategies for 2025
|
https://www.lisamasiello.com
|
[
"Keshav Gupta"
] |
AI for recruiting is the application of artificial intelligence on talent acquisition. One can train machine learning to understand how to shortlist your ideal ...
|
Artificial intelligence technology is the future of recruitment. More and more recruiters are implementing AI for hiring. Some of the benefits recruiters can achieve are:
Saving recruitment time by automating high-volume tasks
Leaders in talent acquisition claim that although they will hire more people in the upcoming year, their recruiting teams will either shrink or stay the same size. As a result, it will be necessary for recruiters to become more effective and, at the same time, do more with less. Manually evaluating applications is still the most time-consuming part of hiring, especially when most applicants for a post are not qualified. A recruiter must spend an average of twenty-three hours reviewing resumes and shortlisting candidates for interviews before hiring.
AI enables you to spend less time on tedious, repetitive chores like automating resume screening, automatically starting evaluations, or setting up interviews with candidates. By using an AI-powered tool, recruiters can automate this portion of their workflow and easily connecting with their current recruiting stack to prevent workflow disruption.
Improving the overall quality of the hiring process
The inability to close the data loop made the quality of hiring a bit of a recruiting KPI black box in the past. Hiring quality has now risen to recruiting’s top KPI, as HR data is easy to access and evaluate. The capacity of AI to use data to standardize the comparison of applicants' expertise and abilities and job requirements holds the promise of enhancing the quality of hire.
Helping with automated messaging
Candidates need to be kept informed about the status of their application as the business moves forward with the recruiting process. Writing messages from scratch to each applicant is impossible.
AI can help in overcoming this problem. AI hiring software can accept template messages and send them to candidates at appropriate times. Even as scheduling interviews or presenting an offer letter frequently involves unique communications, your AI hiring system allows you to send and record these messages.
Helping with resume pairings
The best part about using AI in hiring is that it makes it simple to check the backgrounds of each applicant. Most solutions can parse resume text, transforming its contents into a uniformly formatted profile. Thanks to this uniformity, you can quickly assess each candidate's abilities, experience, and qualifications rather than having to sort through the background information presented on each unique resume.
Helping ensure the right fit
You can carefully evaluate not only a candidate's resume but also their web presence and overall fit. Best Fit AI can help an employer close these gaps and have a more complete view of the candidate. For instance, the AI tool might recognize a candidate's enthusiasm for promoting the company’s messages across various social media channels.
Connecting with targeted candidates
Artificial intelligence is revolutionizing the hiring process as recruiters and hiring managers can find the most qualified individuals like never before. Using AI, they can now target searches on job title, industry, location, earnings, educational background, age, and more. The drawback for the applicant is that the recruiter can do this without having to speak to them.
Inspecting the candidate’s character
The ability to employ a distributed workforce will continue to increase across service-based businesses. Highly effective AI tools are already helping lessen company concerns over hiring remote workers with its ability to inspect a candidate’s character. It can aid in determining whether possible hires will be truthful, moral, and represent the business favorably.
| 2022-12-01T00:00:00 |
https://www.lisamasiello.com/insights-blog/artificial-intelligence-in-recruitment-and-hiring
|
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|
9,000+ Artificial Intelligence jobs in New York, United States (174 new)
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8,000+ Artificial Intelligence Jobs in New York
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https://www.linkedin.com
|
[] |
9,000+ Artificial Intelligence Jobs in New York, United States (174 new) ; Meta. New York, NY $105,000 - $156,000. Actively Hiring ; Sigma AI. New York, NY.
|
This button displays the currently selected search type. When expanded it provides a list of search options that will switch the search inputs to match the current selection.
Jobs
People
Learning
| 2022-12-01T00:00:00 |
https://www.linkedin.com/jobs/artificial-intelligence-jobs-new-york
|
[
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"date": "2022/12/01",
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|
What is artificial intelligence in recruitment? - Greenhouse
|
What is artificial intelligence in recruitment?
|
https://www.greenhouse.com
|
[] |
There is still a lack of trust in AI, especially when it comes to hiring decisions. One of the biggest concerns among candidates and talent acquisition ...
|
Understanding AI in recruitment
What exactly do we mean when we talk about artificial intelligence (AI)? Here’s a simple definition from Mona Khalil, former Head of Data Science at Greenhouse: “AI is an automated system that performs a task you’d typically expect some human intelligence to perform.” And while there’s been a lot of buzz in recent years about generative AI like ChatGPT, other forms of AI like machine learning have already been around for quite some time. In fact, if you’ve ever seen recommendations from news sites or streaming services, those are examples of machine learning in action.
When it comes to the role of AI in recruitment specifically, there are a lot of conflicting feelings. A report from Harvard Business Review shows that 91% of key HR decision makers “believe optimizing hiring processes with automation and AI is necessary for long-term business success.” At the same time, research from Pew on AI sentiment in the US shows many people reporting feeling “wary and uncertain of AI being used in hiring and assessing workers.”
Greenhouse data reflects these mixed feelings. In a survey of candidates and HR leaders, we found that 62% of HR leaders believe that AI can help them hire the best candidate. At the same time, 31% of candidates are worried that a company using AI might reject their application.
Here’s how Madeline Laurano, Founder of Aptitude Research, sums up her take on the role of AI in recruiting: “The more that you can become a champion for AI, get curious about AI, question AI, become skeptical about AI, the better you’re going to be at your job…and [improve] the candidate experience as well.”
Challenges and risks in AI recruitment
While AI offers speed and efficiency, it also comes with challenges, risks and ethical considerations. There is still a lack of trust in AI, especially when it comes to hiring decisions.
One of the biggest concerns among candidates and talent acquisition professionals alike is the potential biases that may influence AI recruitment tools. “These models are only as good as the data they’re based on,” said Tony Hobley, Chief DE&I Officer at Omnicom Precision Marketing Group.
What impact might this have on hiring? Here’s how Dr. Stacie CC Graham, the former Global Director of the Racial Equity Programme at WPP, explained it: “If we look at what made someone successful 20 years ago, that will have excluded a lot of people who may have been caretakers, who may have had physical conditions that kept them from being able to be in the office 10–12 hours a day. That’s already in the data, so if machine learning is based on what made someone successful in the past, it’s likely going to be based on things that many of us would advocate against today.”
And when it comes to generative AI like ChatGPT, it’s worth considering that it tends to create content without a lot of variability or individual expression. Mona Khalil said, “If you just take the recommendations of a generative AI system like ChatGPT at face value, the content out there is going to look the same.” This can make it harder for both job seekers and companies to differentiate themselves if they’re both relying on generative AI for their written communication.
What can you do to mitigate these risks and ensure fairness with AI recruitment? Make sure that any tool you use has comprehensive bias monitoring. You can – and should – also actively monitor the impact of your use of that tool.
AI tools for recruitment
AI tools can enhance efficiency in almost every step of the recruiting process. Here are just a few examples of popular AI-powered recruiting tools.
Sourcing and surfacing candidates
AI sourcing tools can help talent acquisition professionals source candidates who have the skills and background they’re looking for. Instead of conducting lengthy searches themselves, they can simply provide a job description or list of desired skills to the tool and it will return relevant results in a fraction of the time. Some of these tools can also automate outreach to kick off the conversation with candidates who appear to be a good match. There are also tools that use AI to connect active job seekers with hiring teams.
Filtering candidates
You can filter candidates through smart searches (available within Greenhouse Recruiting). This makes it easier to narrow down candidates in a transparent, explainable and compliant way using objective information like the suggested keywords based on the public job descriptions. Ariana Moon, Vice President of Talent Planning & Acquisition at Greenhouse, said, “This is a game-changer because there are so many inbound applications right now.”
Anonymizing resumes (to limit bias during screening)
Resume anonymization is an AI-based recruiting tool that redacts personally identifiable information, such as first and last name, gender and candidate photo. Using a tool with this feature reduces the likelihood that you’ll be influenced by unconscious bias and gently nudges you to focus on candidates’ skills instead.
Generating interview insights and summaries
A tool like BrightHire’s Interview Assistant provides interview summaries and highlights, which eases some of the burden of taking notes during interviews, making it easier for hiring managers and other interviewers to be present during their time with candidates while still making data-backed hiring decisions.
Writing more inclusive job descriptions and employer branding content
Tools like Textio use AI to identify biased language with a large language model. You can run any written text through this tool to identify biased or problematic language and get ideas on how to reword it.
“While AI recruitment software has undoubtedly revolutionized the hiring landscape, it’s essential to remember that even the most advanced AI solutions can’t guarantee a flawless interview experience,” wrote HR consultant Steve Goldberg. In other words, no AI tool can be a substitute for a thoughtful and well-structured recruiting process.
The role of AI in talent acquisition
Now that we have a better understanding of some of the AI tools that are out there, let’s consider how AI is impacting talent acquisition in general.
One of the most important things to understand about AI in talent acquisition is that it’s not just TA teams that are turning to these tools – candidates are increasingly using AI, too. And this is transforming the TA landscape.
AI has made it easier than ever for candidates to apply for jobs, with 38% of job seekers mass applying to roles, flooding employers with resumes rather than pursuing targeted opportunities. According to our own research at Greenhouse, 35% of candidates said it felt fair to use AI in their applications since companies were probably using AI to sort through their resumes. Fair or not, the end result is the same: driving up the number of applications for every open role and increasing recruiters’ workload.
The good news is that there are plenty of ways AI can save time for TA professionals, including by generating first drafts of job descriptions and candidate outreach, summarizing the key points from interviews and removing the back-and-forth of interview scheduling by automatically suggesting optimal meeting times.
If you’ve been wondering how you might start using AI for talent acquisition (if you aren’t already), be aware that you may encounter resistance both from a technical or ethical perspective. When it comes to the technology, Mendy Slaton, People and Talent Operations Leader at Lattice, said that at Lattice they’ve been creating short video tutorials about use cases for AI and sharing them widely. This has helped people feel more comfortable understanding what AI is capable of and how to use it.
If the resistance you encounter is more along ethical lines, you might find it useful to start a cross-functional AI committee that can identify safe ways to experiment with AI in your company.
Balancing AI and human-centric recruitment
At its core, recruiting is about finding the people who will help your company succeed – it’s a human-centric process. That’s why it’s critical to find the right balance between AI and human involvement.
Here at Greenhouse, we’ve given a lot of thought to this topic and published guidelines for using AI in our interview process. Here are a few key points:
Our Talent Acquisition team takes great care to be methodical with how we use AI in hiring. We have no evidence to believe that AI is capable of making reliable end-to-end hiring decisions without human intervention, and therefore we believe AI should supplement – not replace – human judgment and decision-making. As AI comes with flaws and risks, including bias, we view it as a co-pilot that can make hiring teams more efficient, not an auto-pilot that eliminates the need for human oversight.
We also outline some of the ways we use AI along with human TA professionals to boost efficiency and create a positive candidate experience. These include:
For job descriptions, interview questions and sourcing messages
Our hiring managers and recruiters may use AI-generated content as a starting point, but our final product does not reflect an unaltered AI-produced output. It always incorporates critical human input.
For resume parsing
Greenhouse uses AI in our resume parsing process to locate resume details that may identify a candidate. This is used to support our resume anonymization functionality. This means some resume details, like names, are kept anonymous to create a fair and equitable evaluation of candidates.
For interview transcription and summary
With candidate and interviewer consent, we use AI to transcribe and summarize our interviews, which lessens the reliance on the note-taking skills of an interviewer and mitigates the hiring delays and potential bias caused by incomplete documentation of interview feedback.
Providing guidance to candidates on their use of AI
Finally, we believe that this transparency should go both ways. We share the ways our TA team may use AI in the recruitment process and we also let candidates know our expectations about how they can use AI throughout the application and interview process. Our guiding principles here are:
We want to know the authentic – not artificial – you
Two-way accountability
Prepare vs. perform
As Ariana Moon, VP of Talent Planning and Acquisition at Greenhouse, summed it up: “We design our interview processes to get to know the candidate’s own intelligence, skills and experience, because we’re most interested in their independent ability to think and perform in real-world settings and how that applies to the job we’re trying to hire them for.”
The future of AI in recruitment
How will AI continue to shape the future of recruitment? Former Greenhouse Chief Product Officer Henry Tsai outlined how we’ve been considering this topic at Greenhouse and how we view the emerging trends and advancements in AI for recruiting. Here are a few highlights from what he shared.
AI content generation
Large language models (LLMs) already enable recruiters to go from zero to one in the structured hiring process by creating job descriptions and interview questions. In the near future, we envision our hiring software will have advanced text generation models built directly into our platform to power faster and more effective hiring.
A recruiter who wants to generate attributes for a specific role can use generative AI to select the correct prompts to create this instantaneously while reducing human bias at this stage of the process. The quality of job postings and candidate outreach can also improve with generative AI because it can tailor outreach to each unique role and use a consistent employer brand voice.
Categorization: reflecting human intention
The biggest challenge with traditional resume parsing (categorizing specific fields from different resumes) is keeping that process fair since everyone’s resume is different in terms of formatting and language choice.
Applying generative AI data analysis now makes it possible to gather the intention behind resume terminology. AI-powered candidate search tools can help recruiters identify potential candidates with related skills and past candidates who reached late stages in similar positions. Grouping related categories – something that used to be challenging for a machine to do without individual instruction – is now much easier. This unlocks equity among all the resumes coming in for a specific role. It also makes skills-based hiring much easier since recruiters can source from a broader set of relevant candidate experiences.
Summarization: using AI to transform data into insights
Giving our users the ability to have a natural language conversation within our system means saving hours and hours of a recruiter’s time. Imagine typing in a sentence to find the best technical candidate who’s also gotten the most “strong yes” scorecards from the team. Or asking for a summary of reports so you can more easily track your hiring efficiency over the last three months. Using AI, our customers can now take a fresh look at all aspects of the structured hiring process in a fraction of the time.
Another exciting area in automation with AI is summarizing interview transcripts. Think about the potential for recruiters being more present in their interviews, knowing AI will be there to help them with note-taking and synthesizing conversations with candidates. Having an objective view of what someone said in an interview will also help reduce bias. This benefit will be felt by candidates, too, with more focused recruiters able to put less time into transcribing answers and more energy into getting to know each person on a human level.
Automation: streamlining complex tasks
If you’ve ever tried to schedule a meeting with more than one other person, you’ve felt the pain that many recruiters feel when setting up times to meet with candidates and hiring managers.
Imagine if the first conversations around panel scheduling were done by the prompting engine in a natural language conversation. “Find my team a time to have a panel discussion about a candidate that suits everyone’s time zone, prioritizes free time over busy and doesn’t book over lunch.” This type of system would break down barriers and interpret calendars more efficiently.
Another use case involves automating the applicant flow at the top of your hiring funnel. This system could turn off the inflow of applications – and more crucially, turn it on when you’re not getting enough applicants or start buying job ads programmatically.
Preparing for the evolving role of AI in recruitment
A quick recap of the Greenhouse perspective: Right now, we don’t believe that AI is capable of making end-to-end hiring decisions without human interventions. There’s just no good business or moral reason to hand the wheel to AI when we are aware of its existing flaws and risks. That’s why we’re intentionally investing in research that drives ethical and sustainable hiring, where AI can assist, but not replace, hiring decisions made by human beings.
No doubt, the advancements in AI technology are transforming hiring at every level. It’s important for any company in the hiring space to use AI as a way to make things better, faster, fairer and more efficient – and do so in ways that benefit companies and candidates alike. At Greenhouse, we’re ready for what’s next. We’re committed to helping our customers utilize advances in AI to get better at hiring while staying ethically responsible and sustainable.
Read more featured resources here:
| 2022-12-01T00:00:00 |
https://www.greenhouse.com/resources/glossary/what-is-artificial-intelligence-in-recruitment
|
[
{
"date": "2022/12/01",
"position": 75,
"query": "artificial intelligence hiring"
}
] |
|
AI and the Future of Hiring - The Association of Legal Administrators
|
AI and the Future of Hiring
|
https://www.alanet.org
|
[
"Madeline Parisi",
"About The Author",
"Associates Llc"
] |
AI tools can have tremendous positive impacts that create more equitable and effective hiring processes.
|
It stands to reason that using artificial intelligence (AI) in the hiring process would eliminate biases. After all, a machine doesn’t have the preconceived notions that humans have baked into their subconscious.
Or can it?
In 2023, the U.S. Equal Employment Opportunity Commission (EEOC) settled its first-ever AI-based hiring discrimination case with iTutorGroup Inc., a Chinese online education platform that is alleged to have programmed its recruitment software to screen out older job candidates.
Following this EEOC decision, providers of AI hiring tools and employers utilizing these tools may be looking closer at whether the safeguards applied to AI technology help — not hinder — their recruitment efforts.
“It is unrealistic to think there won’t be bias in the data set. You can’t be ‘free from bias,’ says John Boyce, Head of People Development at AMSOIL, Inc. “The best you can do is get very transparent about the biases that are in the data set and try to understand the methodology used in sampling and building the algorithm.”
AI tools are not thinking and creating independently — yet. The AI tool requires training on search criteria, the information to collect and how to fulfill replies. Currently two methods are employed to train AI: One is data scraping, using external data publicly available, and the other uses internal, historical data only.
“Artificial intelligence itself does not contain bias; however, the data it is trained on absolutely can contain bias and could cause adverse selection,” says Matthew Spencer, Co-Founder and Chief Executive Officer of Suited, Inc., an AI-based assessment provider of supplementary data to law firms regarding candidate behavioral traits and relevant cognitive skills. “A key focus of any hiring tools using AI needs to be centered around training the models on well-defined and fully understood data sets.”
For example, Spencer says solutions with data trained on large language models or other types of generative AI (which are two newer technologies) are often trained on massive data sets, the content and relationship of which is not fully understood. “The result can be outputs that are inconsistent, unexpected and unexplainable. Alternatively, tools that leverage machine learning technology (a much more understood form of AI) and are often trained on defined data sets, result in consistent, expected and explainable outcomes.”
You have heard about ChatGPT — which is a large language model — hallucinating. These hallucinations result when artificial intelligence systems generate outputs that are not grounded in reality and are unexpected, or do not correspond to the input data, creating inconsistencies. AI-produced hallucinations can arise due to limitations in the training data, model architecture or optimization processes, causing the AI to produce such unexpected or illogical outputs.
Concerned over AI algorithm use, in 2021 the EEOC launched its Artificial Intelligence and Algorithmic Fairness Initiative to ensure Automated Employment Decision Tools (AEDT) comply with federal civil rights laws. New York City also enacted Local Law 144, which prohibits employers and employment agencies from using an AEDT in New York City unless they ensure a bias audit was done and provide required notices. The law was enacted in 2021, but enforcement began on July 5, 2023.
AI AND THE HIRING PROCESS
Boyce has not moved AMSOIL into AI for hiring because of concerns regarding transparency and not expecting AI systems to be more ethical than the people who develop the systems. “Ethical decisions cannot simply adhere to a predefined series of rules and procedures. Novel situations will come up and I don’t trust AI to make the right ethical decision, especially with its inability to take responsibility for decisions. Humans may be less accountable saying, ‘It’s the algorithm, not me.’ That is a problem for me.”
Given the unique requirements of many roles and responsibilities in the legal community, more novel situations are often the norm. Yaima Valdivia, Principal Software Engineer at Vercara, believes that by incorporating a wide variety of data that captures the full spectrum of potential candidates — including those considered outliers — AI may be trained to handle unusual cases.
“Artificial intelligence itself does not contain bias; however, the data it is trained on absolutely can contain bias and could cause adverse selection.”
Valdivia notes that diversity in training helps the AI learn to evaluate candidates fairly and without undue bias toward the majority. By including provisions for exceptions, the AI system may accommodate a broad range of candidates, ensuring that the system is inclusive. “Failing to account for anomalies can lead to a system that unfairly discriminates against candidates who do not fit within a narrow set of parameters, undermining the fairness and integrity of the hiring process.”
As the firm’s hiring process is assessed and evaluated, so should the tools designed to assist in that process. AEDT tools should not be exempt from preacquisition assessment or ongoing audits. For example, in Suited’s audit process, Spencer indicates that every machine-learning driven candidate-scoring model that Suited develops is examined by an independent auditor, and the audits are conducted in compliance with New York City Local Law 144 on at least an annual basis.
“The audits for law firms are conducted by scoring a pool of more than 14,000 law candidates who have completed Suited’s assessment as part of our clients’ hiring processes,” says Spencer. “Each candidate is scored using the individual AI-model, and the scores are compared across demographic groups to determine if the algorithms are scoring candidates differently by demographic groups, in accordance with the formula set out by NYC LL 144. We also test every model against the EEOC prescribed formula for testing for adverse impact.”
Suited provides the audit reports to their clients, to uphold that no adverse impact is being caused. The report also provides documentation to clients where law or jurisdiction regarding the use of AI tools is applicable.
“AI systems inherently reflect the values, biases and ethical considerations of those who develop and train them. While we can design AI to operate within predefined ethical guidelines, its ability to be ‘more ethical’ is limited by the scope of its programming and the data it’s trained on.”
AI tools make the hiring process more efficient, but is time saved worth the real or perceived lack of communication and interpersonal requirements of a law firm? Attorney Heather Parker of the Parker Law Office, LLC, has not considered implementing AI. Parker’s main reason is that the practice area is very interpersonal and is concerned “there would be a lack of genuineness felt by the candidate or that the communication would not ‘sound like me.’ We each have a style and uniqueness about us, and I would not want that to be lacking in communication with someone who might ultimately deal with my clients and the rest of my team,” she says.
AMSOIL and Boyce are not rushing to utilize an AI tool in hiring, believing that the ethical issues are significant and not enough attention is being paid to them yet.
Given Boyce’s concerns, can we expect AI systems to be more ethical than the people who develop the systems or than the people who input the data?
“AI systems inherently reflect the values, biases and ethical considerations of those who develop and train them. While we can design AI to operate within predefined ethical guidelines, its ability to be ‘more ethical’ is limited by the scope of its programming and the data it’s trained on,” says Valdivia.
The moral responsibility of the AI tool provider is significant, according to Valdivia. It includes ensuring that the AI is developed with an awareness of potential biases and that efforts are made to mitigate these biases through diverse data sets and continuous monitoring. “Providers must also adhere to ethical guidelines prioritizing fairness, transparency and accountability in AI systems,” she says.
As part of the ethical considerations, Valdivia believes that transparency is crucial, indicating the candidate be advised that AI is involved in — or is making — the hiring decisions. This transparency respects their right to understand how decisions about their candidacy are made, and to withhold this information denies candidates the opportunity to provide informed consent and potentially to challenge decisions that they believe to be unfair.
GOING FORWARD
AI-assisted hiring is only one piece of the larger conversation around the use of AI in organizations. So where is AI hiring going and how do we prepare? Valdivia agrees that as AI advances, organizations, developers and legal professionals must work together to ensure that AI tools are used to enhance fairness and inclusivity in hiring processes. “Ethical AI use in hiring benefits candidates and enriches organizations by promoting a diverse and capable workforce.”
Spencer supports the benefits to candidates and organizations. “When properly built and deployed, AI tools can have tremendous positive impacts that create more equitable and effective hiring processes. They can help firms make more accurate and less biased hiring decisions, resulting in increased performance, reduced attrition, greater diversity and improved efficiency.”
In the implementation and use of an AI tool, it is important to recognize that AI is your assistant in the hiring process and does not replace human intervention and interaction. As author Adam Grant indicates in his latest book Hidden Potential: The Science of Attaining Greater Things: “An algorithm is an input to human judgment – not a substitute for it.”
When used with transparency, and the data sets and output are frequently tested, AI may be an additional resource in the hiring process. Are you ready for AI to be your assistant in the hiring process?
Getting the Most From AI in Legal
Want more AI conversation? Tune in to Legal Management Talk, where we recently talked with Kriti Sharma, Chief Product Officer of Legal Tech at Thomson Reuters, where we discuss AI software, what the regulation landscape looks like and how AI will change the future of talent recruitment. Check out Part 1 and Part 2 to hear the full conversation.
| 2022-12-01T00:00:00 |
https://www.alanet.org/legal-management/2024/june/features/ai-and-the-future-of-hiring
|
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|
AI Recruitment for faster hiring - Zoho
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AI Recruitment for faster hiring
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https://www.zoho.com
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[] |
Our AI recruitment software allows you to perform structured hiring based on your needs. The built-in automation and effective communication tools help ...
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Chatbots Keep candidates engaged right from the get go
Easy to configure, straight to the point – our chatbots will strike up conversations effortlessly with your candidates. Candidates can upload resumes, find matching jobs, register for the candidate portal, keep track of their applications, and take a pre-screening assessment, all from within our chatbots.
| 2022-12-01T00:00:00 |
https://www.zoho.com/recruit/ai-recruitment.html
|
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|
How AI is Refining the Process of Hiring on a Low Budget
|
How Artificial Intelligence is Refining the Process of Hiring on a Low Budget
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https://www.recruiter.com
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[] |
Using AI-powered tools in the interview process can help a company analyze potential applicants more quickly. A pre-screening method can help minimize ...
|
How Artificial Intelligence is Refining the Process of Hiring on a Low Budget
Want help with your hiring? It's easy. Enter your information below, and we'll quickly reach out to discuss your hiring needs. Get Started That's not a valid work email account. Please enter your work email (e.g. [email protected]) Please enter your work email
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Artificial intelligence (AI) is the current buzzword in the business world. AI—or clever robots that can reason instinctively and make intelligent sense of massive data—is the new battleground that is churning many sectors, disrupting enterprises, and fueling new rivalries in the networked digital era amid a proliferation of smart gadgets and surge of Big Data.
It’s severe enough for Mark Zuckerberg of Facebook and Elon Musk of Tesla to quarrel over it. Google and Amazon are battling it out with their AI-powered virtual assistants. Consider how the automobile and industrial industries are being disrupted by driverless automobiles and robotized assembly lines.
Similarly, companies are now using artificial intelligence to identify the finest people in their hiring procedures. AI allows recruiters to acquire more information about candidates, allowing them to make better selections.
Recruiters may save more than30% of the cost per hire while enhancing candidate experience and putting superior leaders into jobs sooner by employing advanced algorithms and machine learning. That’s why every extensive and small businesses are not adopting AI.
So, How Is AI Linked to the Recruitment Process?
Recruiters use AI in the talent acquisition process, where machine learning may learn to select your ideal applicant and automate manual processes in the hiring process.
This technology is intended to expedite or automate some aspects of the recruiting process, particularly those that are repetitive and high-volume.
For example, software that does sentiment analysis on job descriptions to uncover potentially discriminatory language or software that applies machine learning to resumes to auto-screen candidates.
AI recruitment tools also use your ATS’s plethora of data to generate insights on your talent pool. Recruiters can use artificial intelligence to help with blind screening to remove bias and visibility into previous successful prospects who have stayed with the company.
Is AI a Good Fit for Today’s Hiring Processes?
It’s probably no surprise that AI has become an increasingly important component of the human experience at practically every turn at this point. Recruiting and job search strategies are no different.
According to research,96% of questioned recruiters believe AI can improve talent acquisition and retention and save them fourteen hours of manual tasks every week.
Obtaining the Best Fit
AI will be able to fill in the gaps for employers by evaluating not only the candidate’s credentials but also their internet presence and overall fit.
For example, a candidate might be very engaged in supporting social causes, which an AI technology would detect. By developing a profile for their beliefs, ambitions, and professional aspirations, a candidate might use AI to determine the greatest fit.
Candidate Assessments
Using AI-powered tools in the interview process can help a company analyze potential applicants more quickly. A pre-screening method can help minimize interviewer biases that could prevent an excellent candidate from being chosen.
Hiring teams can share pre-screening digital interviews at any moment, allowing them to check for transparency in the interview process and how the candidate has handled distant interviewing. AI helps you hire only the best candidate for the job.
Screening of Resumes
Many people will send their applications in response to excellent job postings. However, many resumes submitted do not match the job requirements.
Recruiters used to spend hours sifting through resumes, most of the time merely reading the pages for a few keywords before determining who would advance to the phone screening or in-person interview stage.
Checking resumes manually is a time-consuming task. It’s also an essential chore that highly compensated recruiters should avoid. AI can assist in automating high-volume processes like these. Machines can be programmed to look for specific keywords and linguistic features previously found in successful applications.
To perform correctly, machine-learning software requires a large amount of data. And as AI services acquire more clients, complete more hiring procedures, and enhance their filters for various roles, they will only grow better.
Targeting and Outreach to Candidates
AI-assisted recruiting can help organizations and talent scouts avoid frequent blunders when finding prospects via improved searches at scale. Recruiters can use these searches to find applicants who have specific characteristics.
By removing any potential internal biases, candidate targeting can also help to boost remote diversity and inclusion initiatives. AI-based candidate targeting can be tailored to narrow down applicants by:
Age and demographics
Industry experience
Job titles
Earnings
Educational background
Enhancements to Online Applications
Keywords, word flows, and other data points are used by ATS (recruiter databases) to analyze and prioritize the thousands of resumes they get online for each offered position.
Expect corporations to monetize these same technologies by repurposing them for job seekers, enabling more effective applications through predictive analytics, as employers use increasingly complicated technology for this aim.
Testing and Ranking of Candidates
Employers are increasingly asking candidates to do tasks before or during interviews to assess their talents and make recruiting decisions based on facts rather than recruiters’ perceptions.
These examinations are frequently conducted using video interviewing technology. AI plays a role in this as well. All of these examinations may be scheduled in real-time with calendar sync, regardless of how many people have applied for a post. If an interviewer is required to be present, candidates can be given a booking link to choose a time that is convenient for them.
Once the tests and interviews are done, candidates must be ranked based on their performance and skills. Well-designed AI software can help remove prejudice from this critical choice.
Adopting AI Hiring Strategies
Here are two ways to maintain a fully human hiring process while adopting and implementing AI hiring strategies:
Plan AI Policies for the Hiring and Recruiting Process
Nobody should allow AI to take over the employment process. Every company and its executives have discretion over how much AI is used in hiring. When starting to employ AI for recruiting methods, keep factors like openness, fairness, and responsibility in mind.
Developing solid and ethical policies helps everyone recognize that AI is merely a tool to aid human processes, not a means to replace the human aspect.
Attempt to Remove Human Bias From AI Systems
Of course, machines do not have prejudices, but it is crucial to remember that humans program computers, which can introduce bias. When companies or recruiting firms are looking for an AI recruiting platform, it’s critical to inquire about the provider’s efforts to avoid human bias in AI.
Some people, including machine learning programmers, may have hidden biases that they are unaware of. Hence providers must make extra efforts to avoid biases wherever possible, such as requiring psychological profiling and predictive analytics.
Gender, color, age, and socioeconomic position are just a few biases that could negatively affect the recruiting process. Therefore everyone’s diligence is essential.
Key Takeaway
HR managers may spend more time on critical responsibilities by using AI to automate many portions of the hiring process and improve candidate experience. Yes, AI and machine learning will someday take over many functions people today undertake.
However, no amount of AI will ever be able to replace the need for human judgment in recruiting decisions. Artificial intelligence, on the other hand, will substantially aid human intelligence in enhancing recruiting and job search operations and speeding up and streamlining the entire process.
Hence, this has resulted in a significant paradigm shift across many sectors regarding quick technological stacks and cutting-edge problem-solving.
Ella James is a contributor at QRG101.
Get the top recruiting news and insights delivered to your inbox every week. Sign up for the Recruiter Today newsletter.
| 2022-12-01T00:00:00 |
https://www.recruiter.com/recruiting/how-artificial-intelligence-is-refining-the-process-of-hiring-on-a-low-budget/
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[
{
"date": "2022/12/01",
"position": 83,
"query": "artificial intelligence hiring"
}
] |
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How AI Is Changing the Hiring Process - GoSkills
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How AI Is Changing the Hiring Process
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https://www.goskills.com
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[
"Gergo Vari",
"Gergo Vari Is The Ceo Of Lensa",
"A Job Search Platform That Uses Ai To Match Job Seekers With Opportunities. He'S An Accomplished Entrepreneur Passionate About Using Technology To Revolutionize The Job Search Process. You Can Connect With Him On Twitter At"
] |
AI systems are being implemented to help HR managers and recruiters filter out candidates, assess their skills, and communicate with them as to the next steps ...
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Artificial Intelligence (AI) has been making waves across various platforms, and ChatGPT is one of the shining stars of this technological advancement. Businesses, including GoSkills, are leveraging AI to enhance productivity and efficiency. ChatGPT, with its ability to handle a broad spectrum of tasks, from answering customer queries to generating creative content, is gaining popularity. Its versatility and efficiency make it an attractive option for businesses seeking to streamline their operations.
Whether you're a business owner or an individual interested in the latest technological trends, AI is a subject that is definitely worth exploring.
What is AI?
Artificial intelligence (AI) is an umbrella term for computer programs that mimic human reasoning. In a real-world application, this consists of analyzing large sets of data, identifying patterns, making predictions based on these patterns, engaging with users through a chatbot or similar programs, and much more.
AI is being implemented in nearly all sectors of activity. Here, we’ll take a close look at how AI is being used in the recruitment process, what that means for HR professionals and job seekers alike, and what the pros and cons are for this new trend.
Candidate filtering – a finely targeted search
Recruiters and human resource managers are tasked with filtering through dozens, if not hundreds, of applications before they can create a shortlist and select candidates for interviews. However, companies such as Lensa Inc. uses machine learning to instantly match talent with the best opportunities. By searching for targeted keywords, they can analyze hundreds of applications, resumes, cover letters, and candidate profiles.
This means that when implementing AI in hiring, recruiters and HR professionals are spared, to a large extent, the time-consuming task of filtering applications. It also means that the ideal candidate can be reduced to a series of keywords that correspond to predetermined criteria. The AI program will filter out those candidates who do not correspond to the ideal candidate profile.
AI programs can also respond to applicants, sending out politely worded rejection emails or invitations to proceed to the next stage of the recruitment process.
The pros
AI eliminates ambiguity and approximation, leading to a more finely-tuned search.
The high-volume tasks of filtering out and responding to applications are handled by the AI program, saving HR professionals countless hours of work they can now dedicate to more intricate tasks.
The cons
AI systems work within a precise set of parameters. While this does eliminate ambiguity, it also stands the risk of diminishing decisions that stray from “convention.” An unwavering reliance on AI removes the chances of hiring anyone who does not match the predetermined criteria yet has that undefinable “special something” that might make them worth pursuing.
A greater emphasis on candidate experience
In the mid-2010s, when millennials started entering the workforce, unemployment figures dipped to below 6%. This marked the first time such a dip occurred in well over ten years. The combination of these two factors – millennials entering the workforce and a significant dip in unemployment – represented a shift in leverage between employers and job seekers. For the first time in many years, job seekers had the upper hand. Additionally, millennials had expectations of their employers that were relatively new to the business world.
This shift in dynamic brought about many movements in the hiring process, one of them being the concern for “candidate experience.” Candidate experience can best be described as the candidate’s feelings or impressions as they go through an organization’s hiring process. In many cases, it constitutes a person’s first impression of the organization. And we all know that we only get one chance to make a first impression.
It is important to point out that in today’s climate, where social media and online communication platforms such as Reddit allow virtually anyone’s voice to be heard, a negative candidate experience can quickly lead to a company developing a bad reputation. News of a company’s bad reputation spreads quickly and is very difficult to undo.
AI allows companies to increase their engagement with each candidate through tools such as a chatbot, feedback survey, and notifications.
The pros
More time can be devoted to each candidate, answering their questions and informing them of opportunities.
Chatbots and surveys allow HR managers to collect feedback on the candidate experience so they can take steps to improve upon it.
The cons
An increase in ways to engage with candidates can easily lead to making the process longer and more complicated than many candidates would care for. Sometimes, less is more.
A reduction in hiring bias
Hiring bias has been a huge problem plaguing recruitment for decades. The advent of AI systems and their implementation in the hiring process goes a long way toward reducing this problem.
Biases come in one of two forms: conscious and unconscious. A conscious bias is when someone has a preconceived idea of someone based on irrelevant factors. For example, someone could be under the misguided impression that women cannot do a given job as well as a man. If the person is aware of this bias, that would be considered a conscious bias.
However, in many instances, we are not aware of the biases we have. Take, for instance, the fictitious example of someone who had a negative experience with someone who graduated from a given university. When they meet someone else who also graduated from this university, they may make a negative association in their minds. They may not even be aware they are making this association. This is an example of an unconscious bias.
When biases enter into the hiring process, candidates are negatively affected because they are not given a fair shot at an opportunity. And companies are also negatively affected because they can miss out on candidates who would otherwise be an invaluable asset to their organization.
All humans are susceptible to biases. AI systems, however, are completely free of biases (assuming they have been well-programmed). AI systems look for and filter out candidates based on specific criteria that are relevant to the job opening and only such criteria. As AI systems become more and more prevalent in the hiring process, we can expect to see a significant decrease in hiring bias which, in turn, means more people being treated fairly and fewer organizations missing out on worthy candidates.
The pros
AI systems do not take into consideration factors that are irrelevant to the job openings they are being asked to help fill.
AI systems assist in making data-driven decisions and not emotional ones.
The cons
AI systems may still be susceptible to biases if the algorithms they use have not been thoroughly checked for potential biases. For example, if an AI system is looking for patterns in resumes received over the past ten years, and in that time, only resumes from a certain demographic have been received, the AI system may be prone to excluding candidates who do not fit that demographic. The AI system is only as reliable as the data sets they are basing their analyses on.
Skill testing
Testing the various candidates to make sure they do, in fact, possess the requisite skills is a time-consuming and often inaccurate endeavor. This important task can be delegated to AI systems.
There are a number of different ways AI can carry out a candidate’s skill assessment, for instance, exam-based questionnaires, chatbot discussions, or work situation simulations. With a bit of creativity, they can even be fun for the candidate which brings about the added benefit of improving the overall candidate experience.
The pros
Less time spent by HR managers and recruiters (time they can now spend on more complex tasks)
A more accurate assessment of a candidate’s skills
Elements of gamification in skill assessment could help boost the overall candidate experience.
The cons
Skill assessment tests (if not well designed) could end up assessing the candidate’s ability to take a test rather than assessing more relevant skills.
Tests can be irrelevant to heavily experienced candidates and might put them off from completing the hiring process.
Less time spent on high-volume tasks means more time for innovation
AI systems are being implemented to help HR managers and recruiters filter out candidates, assess their skills, and communicate with them as to the next steps in the hiring process. This means that the HR manager or recruiter has more time to spend on more complex or creative tasks. How they spend this newly acquired time is up to them.
As AI in the hiring process becomes more and more prevalent, we can expect to see HR managers and recruiters spending their time in creative and innovative ways. It should then comes as no surprise that in the last ten years, we have seen a greater amount of innovation in the field of recruiting than we have ever seen before. This trend is likely to grow and expand in ways that are limited only by our imagination.
Facilitate AI in the training process
AI is being implemented in the hiring process because of its effectiveness and the fact that it helps HR managers save countless hours of work. For these very same reasons, AI is being used to train new employees for long-term success.
When AI is used in the hiring process, it makes for a smooth transition to AI used in the training process, and employee training does not stop once onboarding has been completed. Employee training should continue throughout the tenure of the employee, facilitated by the use of a learning management system (LMS).
Conclusion
We have only scratched the surface of what AI can do in the hiring process: save time, improve candidate experience, and reduce hiring bias. As HR managers and recruiters are relieved of the burden of high-volume tasks, thanks to AI, we can expect to see an increase in innovation in both the hiring and the training process.
| 2022-12-01T00:00:00 |
https://www.goskills.com/Resources/Using-AI-for-hiring
|
[
{
"date": "2022/12/01",
"position": 85,
"query": "artificial intelligence hiring"
}
] |
|
AI Recruiting: The Power of Artificial Intelligence in Hiring - VireUp
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AI Recruiting: The Power of Artificial Intelligence in Hiring
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https://www.vireup.com
|
[] |
AI recruiting involves the strategic incorporation of advanced algorithms and machine learning into the hiring process to deliver better results more ...
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If you want to hire a brain surgeon and only five people on the US East Coast can do it, this article is not for you. But if you are an overwhelmed recruiter who has to deal with hundreds or thousands of candidates, we have the solution.
In comes AI recruiting. Let's explore the key ways it can help you hire the best candidates most efficiently, along with common objections and solutions for them.
What is AI recruiting?
AI recruiting involves the strategic incorporation of advanced algorithms and machine learning into the hiring process to deliver better results more efficiently. AI uses computer algorithms to analyze resumes or job interviews quickly in a similar way as an essay grader , interact with candidates and predict who might be the best fit for a job. By automating mundane and administrative tasks, artificial intelligence helps companies to find the right people faster and with less bias.
It's like having an intelligent assistant who augments the talent acquisition team's capabilities by helping to do more work with the same amount or fewer resources.
AI recruiting: Use cases in the recruitment process
AI in recruiting has many use cases that enhance efficiency, objectivity and overall effectiveness while hiring:
Sourcing job seekers
To find the best fit, you may need to search for and attract candidates and expand your talent pool. AI-powered tools offer a range of features to streamline this process. You can use filters to narrow down candidates based on various criteria, such as industry, skills and experience.
Additionally, you can use a Boolean search, which allows you to combine words and phrases using the words AND, OR and NOT to refine your search further. Another valuable feature is the ability to receive suggestions for similar profiles based on your searches, which lets you uncover potential candidates who may not have appeared in your initial search but share relevant characteristics.
Screening resumes in a flash
AI recruiting software quickly reads and understands resumes using natural language processing (NLP) technology. They pick out important details like skills, qualifications and work experiences from resumes, helping recruiters get a fast and clear overview of a candidate's background.
To speed up the hiring process, AI compares candidate applications with predefined criteria, like job requirements and qualifications.
AI reviews past performance and skills to predict candidate fit and highlight likely successful hires.
Also, AI algorithms analyze historical data to predict a candidate's potential fit for a specific role based on their skills, experiences and past performance. This helps recruiters identify candidates who are likely to succeed in the position.
One-way video interviewing
If you're conducting regular interviews, you're probably accustomed to sitting in a room for hours, watching potential candidates answer your predetermined questions and filling out many interview evaluation forms.
By leveraging AI, you can forget about screen fatigue and missing important details because you lose concentration – AI platforms don’t get tired, and they can rate every candidate based on the same criteria. You’ll save hundreds of hours to focus on things that matter most, like strategic tasks and employee relationships.
One of the best comprehensive solutions for one-way video assessment is VireUp's video interviewing software. This is why:
Competency-based questions: VireUp assists recruiters in creating competency-based interview questions tailored to different positions. That way, you'll always have a standardized evaluation process. Effortless screening: This skill assessment software uses AI to analyze the entire interview, sentence by sentence. Recruiters benefit from an explainable AI that shows evidence of its conclusions, so you don't have to watch each interview. Language assessment: VireUp automatically assesses spoken English proficiency as applicants respond to interview questions. Coaching-level feedback: Most applicants (94%) want to receive interview feedback, but only 41% do, according to LinkedIn research. This is your chance to stand out and position yourself as an employer of choice with VireUp's coaching-level feedback, which offers valuable insights into candidates’ performance. AI recruiting video interview platform: VireUp
Here is how you can use AI recruiting tools to interact with potential candidates:
Chatbots for interaction: AI-driven chatbots engage with candidates on company websites or application portals, answering common candidate questions and providing information about the hiring process. Status updates: AI can offer status updates on candidates' applications, keeping them informed about the progress of their recruitment process. This helps to maintain transparency and manage candidate expectations. Feedback collection: AI tools can collect feedback from candidates about their hiring experience. This feedback helps recruiters improve their processes and provides insights into the candidate's perspective. Personalized communication: AI can analyze candidate data to personalize communication, tailoring messages based on the candidate's preferences, experiences and interactions with the company. This creates a more individualized and positive experience. Company feedback: AI recruiting software can send personalized feedback automatically once the recruitment process is done. That way, even rejected candidates feel heard and appreciated by the company. They are more likely to apply for a job again when they improve their skills or even be a customer of the company at some point. AI handles candidate engagement: chatbots for questions, status updates, feedback collection, and personalized communication for a more positive hiring experience.
Creating and posting job ads
Here’s how AI can help you with job ads:
Job ad content optimization: AI-powered recruiting software can analyze successful job postings and suggest relevant keywords, formatting and unbiased language to ensure that the content appeals to a diverse range of qualified candidates. Automated job posting: AI enables the automated posting of job ads across multiple platforms, including job boards, social media and company websites. Performance analytics: AI recruiting software provides analytics on the performance of job ads, tracking metrics such as views, clicks and applicant conversions. Real-time market insights: AI and machine learning tools provide data-driven insights into current market trends, competitor job postings and salary benchmarks. A/B Testing: AI can perform A/B testing of different job ad variations to identify which elements, such as headlines or descriptions, have the most effect on job seekers.
Scheduling
When trying to hire a lot of people at the same time, you can get into a complete mess trying to coordinate scheduling interviews, let alone conducting them. AI tools automate the scheduling of interviews by analyzing the availability of both candidates and interviewers, considering time zones and preferences to find suitable time slots and minimizing back-and-forth communication.
Integration with calendars ensures efficiency, and some tools enable candidate self-scheduling for added flexibility. AI also facilitates panel interview coordination and integrates with Applicant Tracking Systems (ATS) for a comprehensive recruitment workflow.
However, thanks to automated and self-service job interviews provided by platforms like VireUp, you don't need to worry about scheduling anymore because it may not even be relevant for many roles.
Why invest in AI recruiting?
Investing in AI recruiting tools offers several benefits for organizations looking to optimize their hiring process:
Saving time and money
Time savings: AI streamlines time-consuming tasks like resume screening, interview scheduling, interview content analysis and candidate communication, allowing recruiters to focus on strategic and high-value activities. With VireUp, you can expect a 40% faster recruitment cycle time. VireUp cuts recruitment time by 40% Cost savings: The efficiency affects cost reduction, of course. By automating repetitive tasks, AI reduces the need for extensive manual labor and minimizes the risk of human errors, leading to cost savings in terms of resources and potential recruitment expenses. With VireUp, you can save up to 45% on recruitment costs.
AI helps recruiting teams to find the most promising candidate, which is very hard, especially if you're recruiting among hundreds of people.
AI generates valuable insights about job applicants through data analytics, helping recruiters make informed decisions. Predictive analytics can identify the most promising candidates, improving the chances of successful hires and reducing turnover.
According to the Society for Human Resource Management's (SHRM) research, nearly 3 in 5 HR professionals say the quality of their organization’s hires is somewhat (50%) or much (9%) better due to their use of recruitment automation or AI.
AI recruitment tools make the candidate experience better by:
Personalizing interactions Ensuring fast communication Facilitating application submissions Collecting and providing feedback Reducing bias
AI can help you achieve an 80% reduction in candidate dissatisfaction. Overall, the result is a more engaging, transparent and positive candidate journey that is essential for employer branding.
Streamlining operations for scalable growth
AI recruiting tools adapt to seasonal demand and support scalable growth.
During specific times of the year, such as the launch of talent programs around the graduation period of universities or the introduction of a new product, recruitment teams often experience demand surges.
AI recruiting tools efficiently handle increased workloads and adapt to these seasonal fluctuations. You can use AI systems according to your organization's changing recruitment needs. This adaptability is crucial for scalable growth.
AI recruiting: Common objections and solutions
Even though there are tons of benefits for hiring managers using AI in the recruiting process, there are also some concerns about AI software solutions:
Bias
Objection: Some people worry that AI may introduce unconscious bias or carry on biases in the hiring process. It is clear why these concerns exist – if historical data used to train AI models already have biases in the selection process, the AI system might carry on those biases, potentially leading to discriminatory outcomes.
Solution: You should implement AI algorithms that are designed to be unbiased, auditable and explainable, so you know why the system made the conclusion it did. The technology should evaluate candidates based on skills and credentials instead of demographic factors. Regularly audit and update algorithms to minimize bias.
Privacy concerns
Objection: Candidates may be concerned about the privacy of their personal data when interacting with AI systems. As AI systems analyze large amounts of candidate information, questions arise about how data is stored, who has access and how it's used. Candidates fear potential misuse or unauthorized access to sensitive information.
Solution: You should clearly communicate and execute data protection measures and ensure compliance with privacy regulations. Implementing secure data storage practices, anonymizing personal information and obtaining explicit consent from candidates regarding data usage are some of the fundamental measures.
To learn more about the ethical considerations and regulations around AI, listen to the webinar I did with the leading experts in the field, the Assistant Director at the Institute for Ethical AI Esra Karahasanoglu and a cross-cultural AI ethics advisor Ayca Ariyoruk:
Cost and implementation
By choosing AI tools that fit your budget you minimize setup hassles.
Objection: Companies might be hesitant to implement AI in the recruiting process because of the perceived high costs and challenges in implementing AI recruiting solutions. In fact, of those who don’t use automation or AI to support HR-related activities, 44% said that they lack resources (time, money, labor) to properly audit or correct AI algorithms, and 34% said that they can’t afford to implement automation or AI, according to an SHRM study.
Solution: Reducing the costs is actually the main value of automation. Choosing cost-effective AI solutions that align with your organization's budget and needs will be immensely critical. Many AI tools offer flexible pricing models and can be integrated with existing systems, minimizing implementation challenges. Bear in mind that the long-term cost savings often outweigh the initial investment.
No personalization
Objection: Some fear AI may lead to a lack of personalized interactions or human touch, affecting the overall candidate experience.
Solution: You can select AI tools with personalization features. AI can be programmed to tailor communications, answer candidate questions, provide feedback and recommend roles based on individual candidate profiles.
Conclusion
If your company is rapidly growing, artificial intelligence-powered tools can help solve many of your problems. By automating different recruitment processes, from sourcing and screening candidates to personalized interactions and interviewing, AI helps to identify the best candidates while saving time, reducing costs and enhancing the overall candidate experience.
Still, there are concerns surrounding bias, privacy, expenses and customization when using AI in recruitment. The solutions are out there, but you need to find the tools that incorporate all of them and suit your needs.
If you want to interview thousands of people while reducing costs and time, VireUp might be the answer. Book a free demo to learn how we can help you make the most of AI.
FAQs
How are companies using AI for recruitment?
A big retail company, Metro, used AI for one-way video interviews – while conducting more than 500 interviews using VireUp AI, the company cut their recruitment time by half and increased their candidate throughput six times.
Apart from video interviewing, companies are using AI for:
Sourcing Screening resumes Interacting with candidates Creating and posting job ads Interview scheduling Creating pre-employment tests
What is the danger of AI in recruitment?
The danger of AI in recruitment lies in the potential reinforcement of biases. If historical data used to train AI models reflect biases in hiring decisions, the AI system may continue to use these biases, leading to discriminatory outcomes in hiring. Additionally, there can occur problems with privacy and lack of personalization.
As we mentioned, there are solutions to all these – it’s a matter of finding a software solution that supports ethical and unbiased decision-making.
Does Google use AI in recruitment?
Google has been reported to use AI and machine learning in various aspects of its recruitment process. It is known for employing advanced technologies to streamline its hiring procedures, from screening resumes to assessing candidates' skills and potential cultural fit.
How big is the AI recruiting market?
The global AI recruitment market is anticipated to grow steadily according to business valuation firms, from its 2022 valuation of $630 million to over $830 million by 2028. AI continues to reshape recruitment strategies, with 79% of recruiters predicting that AI will soon play a pivotal role in hiring and firing decisions, while other trends such as the rise of mobile recruiting and social media are transforming how companies attract top talent.
Why is AI recruitment important?
AI recruitment is crucial for the hiring industry because it:
Automates tasks like resume screening, candidate sourcing and interview scheduling Significantly accelerates the recruiting process Enhances decision-making with its predictive analytics Identifies top candidates efficiently Minimizes biases in candidate evaluation, promoting fairness and diversity Improves the candidate experience by providing regular updates and feedback
Image sources: Freepik, Unsplash, Pexels
| 2022-12-01T00:00:00 |
https://www.vireup.com/blog/ai-recruiting
|
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|
How To Use AI Recruitment To *Actually* Automate To Save Time
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How To Use AI Recruitment To *Actually* Automate To Save Time
|
https://vervoe.com
|
[
"Siobhan Carlson",
"Siobhan Is A Dynamic Marketing Professional With Over Years Of Experience Spanning Small Businesses To Global Saas Enterprises",
"Tech Startups. Throughout Her Career",
"She Has Honed Her Skills Across The Marketing Funnel",
"Specializing In Brand",
"Strategy Ownership",
"Multi-Channel Demand Generation",
"Content Marketing",
"Account-Based Marketing",
"Performance Marketing."
] |
AI recruiting is the practice of integrating artificial intelligence, such as machine learning or predictive algorithms, to the time-consuming or resource- ...
|
Many talent, recruitment and HR teams have implemented an AI recruitment solution to improve their hiring practices.
But how do you actually use AI to automate recruitment and save time?
What is AI recruiting?
AI recruiting is the practice of integrating artificial intelligence, such as machine learning or predictive algorithms, to the time-consuming or resource-intensive parts of hiring.
There are a variety of AI recruitment tools that assess a candidate’s suitability for a role. Essentially, these tools use algorithms to understand data points from responses, including:
Analysis of facial expressions, body language, and gestures
Voice and text sentiment analysis
Actual ability to do the job
Cultural fit and personality
But that’s not why recruiters are using AI. Recruiters and talent teams want to use AI to automate the hiring process to make it faster, less expensive and easier to manage high volumes of candidates.
Why AI for Recruitment?
AI, or artificial intelligence, can be a powerful tool to drive efficiency in recruitment. By reducing the time spent on screening, skill testing and interviewing, HR and Talent teams can focus on ways to attract, onboard and retain employees.
As AI technology becomes more accessible, all industries are using AI to reduce or remove time-consuming activities. The world of recruitment is no different.
Talent acquisition teams are deploying AI recruitment tools to get through the most time-consuming aspects of the recruitment process. Pre-employment screening of candidates remains the biggest drain on recruiter resources. The number of candidates per open req has increased, and finding quality candidates has become more challenging.
To help you understand the landscape of AI recruitment technology, we put together this definitive guide.
Adopting and optimizing technology is a huge opportunity for recruitment. There is a broad range of AI-driven tools available to today’s recruiters to help make the hiring process more scientific, scalable and effective.
How recruiters are using AI
There are three main ways recruiters are using AI in the hiring process:
Sourcing: finding and connecting with talent quickly
Screening: quickly deriving the best applicants
Interviewing: facilitate remote hiring and save time
Candidate sourcing
AI-based tools for sourcing help recruiters find and connect with talent faster. Some tools focus on searching for profiles across job boards or internal databases to fill an open position. Others help maximize marketing efforts and connect with candidates in real-time. And there are AI chatbots that can interact with a candidate to determine what specific role will be the best fit and show them how to apply through the job site.
Some examples of AI sourcing tools include:
Hiretual
Customers use Hiretual to source across 40+ platforms and 700M+ professional profiles and hire efficiently with team collaboration, manage talent pool, customize candidate engagement, and rediscover candidates in their ATS/CRM.
Customers use Hiretual to source across 40+ platforms and 700M+ professional profiles and hire efficiently with team collaboration, manage talent pool, customize candidate engagement, and rediscover candidates in their ATS/CRM. Appcast
Appcast uses predictive analytics, real-time data and programmatic bidding to maximize recruitment results.
Appcast uses predictive analytics, real-time data and programmatic bidding to maximize recruitment results. Shapr
Not limited to candidate sourcing, Shapr is sort of a Tinder for professional relationships. The machine-learning algorithm suggests 15 relevant people to meet each day, and communicate when interest to connect is mutual.
Candidate Screening
As shown, screening is the most time-consuming aspect of the hiring process. AI screening tools aim to quickly derive information from applications to speed up this step.
AI screening tools range in approach, from resume parsing to behavioral and skill assessments. Predictive performance based on skill testing tends to be a better way to match candidates with open positions, as a skill test indicates current knowledge and ability, versus the historic nature of resumes. Either way, AI tools streamline the process and help screen candidates in, not out.
Some examples of AI screening tools include:
Recruiterbox
Has resume parsing functionality within their applicant tracking system, as well as flexible application forms to help screen candidates.
Has resume parsing functionality within their applicant tracking system, as well as flexible application forms to help screen candidates. Ideal
Ideal’s candidate screening software provides automated resume screening to shortlist candidates.
Ideal’s candidate screening software provides automated resume screening to shortlist candidates. Vervoe
AI-based skills assessments that let you evaluate at scale, and spend more time with high performing candidates. Instantly auto-grades and ranks candidates according to job-related skills.
Candidate Interviews
AI tools are used in interviews in two key scenarios. In the first instance, companies like Unilever, Google, and Facebook have begun to use AI to assess video interviews, using voice and facial expression analysis to assess personality traits. While this can help cut down on the time and attention required of recruiters to review each candidate’s recorded response, there are some big red flags with using facial recognition to select candidates – more on that in a minute.
The second way to use an AI tool with a pre-recorded video interview is to analyze the content of the answer. For example, a skills test may ask a candidate to write a sample blog post on a topic relevant to the company’s industry. Then, in a pre-recorded video interview, the tool asks why the candidate selected the topic they chose to write about. The video portion can be used to give more context to the candidate’s skills test and help a candidate stand out beyond their initial response.
Some examples of AI candidate interviews are:
HireVue
Digital interview platform. HireVue uses AI in their interviews to analyze work style and cognitive ability.
Digital interview platform. HireVue uses AI in their interviews to analyze work style and cognitive ability. VCV
Uses chat and a phone calling bot to phone screen candidates, then invites them to video interviews with facial recognition.
Uses chat and a phone calling bot to phone screen candidates, then invites them to video interviews with facial recognition. Impress
Uses a chatbot to conduct text-based interviews with candidates.
These uses of AI in recruiting are just the tip of the iceberg.
Recruiting is now the biggest AI market in HR, with AI-based sourcing, assessment, screening, interviewing, and candidate experience management now available. Josh Bersin HR Technology 2020: Disruption Ahead
Challenges of applying AI in recruitment
1. It can take time to gather data for AI
In general, AI needs plenty of data to learn from. Machine learning algorithms rely on getting thousands or millions of data points to accurately mimic human intelligence.
Depending on your vendor, this means there might be a lengthy implementation process. Algorithms that rely on existing internal data, like testing your high performers, are costly and time-consuming.
This can also help you decide at which stage of the funnel you should use your AI tool. Because more data is required, it’s best to use AI recruitment tools at the top of your funnel. Not only will you cut screening time, you’ll ensure a bias-free and accurate result.
2. The right data needs to be used to predict outcomes
The most important question in using AI tools is: am I giving the algorithm the right data, and does that data accurately predict the outcomes I want to achieve?
An example of questionable data choice lies in facial analysis. Recently, there have been complaints about this technology, with experts worried the systems could unfairly penalize candidates and hide biases in how they assess ideal candidates for recruitment.
Modeling based on top performers also stifles innovation and growth. Instead of assessing ability, a tool that seeks to hire people based on high performers reduces diversity by promoting “look-alikes” of existing employees.
Ensure you understand what data your vendor uses for the learning algorithm. Importantly, candidate age, gender and race should never be taken into account in AI recruitment.
3. AI can potentially replicate human bias
The biggest issue that arises is when some AI tools are used to interview candidates is in multiplying or replicating human bias. The notorious example of Amazon scrapping their AI recruiting tool after it was discovered to have been replicating the biases found in the human recruiting processes is just one example. The flaw in this tool wasn’t the technology; it was the data being used to feed the algorithm.
Ensure your vendor understands the risks of bias issues and has a process to identify and mitigate patterns of bias.
What roles can you use AI recruitment for?
AI recruitment tools can be used for any industry. However, some positions are harder to assess than others. According to one expert, professional industries with fewer candidates, such as IT, nursing, and senior-level positions are more complicated to hire for – and therefore may not be the best places to use AI interviews.
The key to using automated interviewing tools is to understand what success looks like in an open position. For example, if you’re hiring a call center rep, the AI tool should be assessing voice cues and qualities like patience. For programmers, an AI tool can prompt coding challenges in a certain language or skill set. Identify what competencies are important for on-the-job performance before using your AI tool to ensure you surface the best candidate.
AI can help evaluate these skills, in a range of formats.
It’s difficult to trick an AI tool, especially when used in a skills test, as the purpose of AI ranking is mostly to screen candidates in, rather than out. However, some machine learning tools have been set up exclusively to prevent cheating on one-way interviews, for example. One automated interview tool detects if the candidate is regularly looking away from the screen (and possibly relying on cue cards) to answer the question. Or, the tool can pick up if there’s another voice on the recording coaching the candidate to answer questions.
Regardless of the tactics that some candidates may try, inevitably the AI tool is part of a larger recruitment process. If a candidate “cheats” or tries to outsmart an AI tool, a recruiter should be able to triangulate a submission with other facets of their application. It’s all part of creating a formula that helps the right candidate stand out from the crowd.
How will AI change the role of the recruiter?
Recruiters won’t be replaced by robots. In fact, AI will help recruiters spend more time on high-value tasks, like enhancing the candidate value proposition, engaging passive candidates, and improving the onboarding experience.
By using AI to remove the repetitive tasks involved in screening candidates, recruiters can:
Spend more time creating great job marketing campaigns
Have deeper conversations with hiring managers to understand roles
Focus on interviewing top candidates, engage them and get to know them
Concentrate on creating a great onboarding experience, enhancing EVP
The best AI solutions will augment human decision making, continuously learn from humans in the environment, and allow recruiters to spend more time with candidates to ensure a great placement.
What’s next for AI recruitment?
Will hiring soon be delegated exclusively to robot representatives? Probably not in the near future. But, according to one AI trendwatcher, it may not be a ridiculous proposition.
In some ways, recruitment tools have already ushered in the possibility of constructing a candidate-side AI tool. A recent article by TheVerge outlines how prospective job applicants have already been reduced to a “series of data points.” Between a candidate’s social media profiles, LinkedIn, and internet history, an employer has a rough outline of a person’s ability before the interview process. It’s not far off to suggest that a candidate may attempt to utilize AI to construct their own digital representation, consolidating their internet presence in a way that best represents them to a recruitment algorithm.
Further Reading
Editor’s note: This article was originally published in July 2019, and has been revamped and updated for accuracy and comprehensiveness.
[Read more: Online recruitment methods]
| 2022-12-01T00:00:00 |
https://vervoe.com/ai-recruitment/
|
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|
The Rise Of AI In HR: The Evolution Of The Hiring Process - HR Future
|
The Rise Of AI In HR: The Evolution Of The Hiring Process
|
https://www.hrfuture.net
|
[] |
AI-powered algorithms can cull resumes, find good internal candidates, high-performing employee profiles, and even decode video interviews and give us insights ...
|
The hype and expectations around artificial intelligence (AI) are very high right now. We talk to our computers; drones deliver our food; our cars are already driving themselves. And what will happen next? Today, every HR solution provider is building an AI team, and we all want our systems to be more intelligent and more functional. I believe this market is still very young. Therefore, I want to point out in this article a few things to consider.
The role of AI in HR and management
Let’s start by saying that AI is not a magical computer person; it is a wide range of algorithms and machine learning tools that can quickly combine data, identify patterns, and optimize and predict trends. Systems can understand speech, identify photographs, and use pattern matching to recognize signals that speak of mood, honesty, and even personality traits. These algorithms don’t use intuition as humans do, but they are fast, so they can analyze millions of bits of information in seconds and quickly identify pattern correlations.
Statistically, AI systems can predict and learn by plotting possible outcomes and then optimizing decisions based on many criteria. So you can imagine an artificial intelligence system that looks at demographics, job history, and interviews with candidates and then predicts how well they will do their job. (HiredScore, Pymetrics, HireVue, IBM, and others are working on this.)
What kind of applications should we expect?
Let me list just a few of the areas in which we could see genuinely breakthrough results.
1. In recruiting, we make a lot of “I feel” decisions. One study found that most recruiters make decisions about a candidate within the first 60 seconds of meeting them, often based on the look, handshake, dress, or speech. Do we know what characteristics, experience, education, and personality traits guarantee success in a given role? No, we don’t know that. Managers and HR professionals use millions of dollars worth of estimates, tests, simulations, and games to hire people, but many say they are still wrong about 30-40% of their candidates.
AI-powered algorithms can cull resumes, find good internal candidates, high-performing employee profiles, and even decode video interviews and give us insights into those who are likely to do well. Several of our clients are now using AI-powered assessment from Pymetrics to select candidates for roles in marketing and sales, and the selection success rate has increased by more than 30% while eliminating the interview bias and educational bias inherent in this process. The role of AI in recruiting will be huge.
While we are all mainly interested in job skills (software skills, sales skills, math skills, etc.), most research shows that hard skills are a small part of a person’s success. In our most recent study on Highly Effective Talent Acquisition, we found that companies in Tier 4 with the highest financial return on hiring allocate nearly 40% of hiring criteria for emotional and psychological traits such as ambition, learning, passion, a sense of purpose. Will AI detect this too? Yes, it is possible.
2. Development and training of employees. We do not yet know perfectly how to “train” people. The global L&D industry is over $ 200 billion, and most educators tell us that at least half of it is lost (misused or wasted developer time). But we don’t know which half!
Do you know what you need to learn to be better at your job? We all have ideas about this, but what if we had algorithms that monitored and learned the skills, behaviors, and actions of the highest performing performers in our teams and then just told us how to be more like them? These “Netflix-like” algorithms are now entering the world of learning platforms and make learning rewarding and as fun as watching cable TV. I repeat, the market is young, but the prospects are huge. Our research shows that the average worker has less than 25 minutes a week for training; if we can make better use of this time, everyone will work better.
3. In management and leadership, we read books, go to workshops, enroll in altMBA programs, copy bosses we admire, and celebrate the successful leaders of the day. Do we know the science of leadership? I would suggest that fleeting themes reign here. This year we are focused on purpose, mission, and support. It was only five years ago that it was “ministry leadership,” even earlier, it was “skill and financial acumen.” Most research shows that dozens of managerial and leadership qualities determine success, and each of us brings a slightly different and unique mix of them.
Will Artificial Intelligence be the hallmark of HR solutions?
The hype around AI is at an all-time high right now. Every software vendor wants you to trust that they have a machine learning team and best-in-class AI solution. These capabilities are critical to the entire industry but don’t believe the hype.
The success of each HR tool will depend on many factors: the accuracy and completeness of its algorithms, the ease of use of its systems, and more importantly, its ability to provide so-called “bottleneck AI” or concrete solutions that solve your problems. It can only be done when the vendor has a massive amount of data (to train his system) and receives a lot of feedback on how well the system performs. Therefore business strategy and customer proximity will be barriers to entry, not just great engineers.
Also, don’t buy a black box system if you can’t prove its usefulness to your company or your market expansion strategy. The decisions of the management and people of each company are often culture-based, so we will need to take the time to try these systems in the real world and tune them for the best use. For example, IBM has spent years optimizing its AI compensation and career solutions for its company, culture, and business model. Now they sell these tools to corporate clients and find that each successive implementation opens up new things to improve for that industry, culture, or organizational need.
Conclusion
Despite these challenges and risks, the growth potential is enormous. Companies spend 40-60% of their income on payroll, and most of this colossal expense depends on management decisions that we make intuitively. As AI systems in human resources become more intelligent, more proven, and more problem-specific, we will see significant improvements in employee productivity, efficiency, and well-being. We have to be patient, vigilant, and keep investing in AI.
Martha Payne is a Personal Growth Coach with 10 years of experience working as a business development professional. She is truly passionate about nurturing talent and ideas that evoke transformative change in individuals, teams, and organizations. Her focus is to help organizations develop leaders for the future – unleashing the full talent, passion, and potential of individuals (in particular Millennials) through tailored leadership development and coaching programs.
Did this article help you? If you’d like to surround yourself with global HR Thought Leaders and Experts whose articles will advise you on the best way forward, subscribe to HR Future digital magazine.
| 2021-08-06T00:00:00 |
2021/08/06
|
https://www.hrfuture.net/talent-management/technology/the-rise-of-ai-in-hr-the-evolution-of-the-hiring-process/
|
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] |
Tech Layoffs 2025: Why AI is Behind the Rising Job Cuts
|
Tech Layoffs 2025: Why AI is Behind the Rising Job Cuts
|
https://www.finalroundai.com
|
[] |
507 tech workers lose their jobs to AI every day in 2025. Complete breakdown of 94,000 job losses across Microsoft, Tesla, IBM, and Meta - plus ...
|
We are halfway through 2025, and nearly 94,000 tech workers have already lost their jobs. But this is not just another round of cost-saving. Companies are quietly transforming their workforce to align with their AI strategies.
Some roles are disappearing because AI tools are doing the work. Others are being cut so that businesses can redirect their spending toward AI engineering, infrastructure, and research.
Let’s look at what has happened so far and why these layoffs are deeply connected to AI.
Month-by-Month Breakdown of Tech Layoffs in 2025
July
Microsoft confirmed plans to cut 9,000 roles, mainly in gaming and cloud, bringing total 2025 cuts to over 15,000. The move is part of a push to simplify operations and invest heavily in AI.
TikTok is planning workforce reductions in July as well. A leaked internal memo revealed the company will cut roles in its e-commerce and marketing divisions. The layoffs follow multiple rounds earlier this year
June
Google reduced about 25 percent of its smart TV team. At the same time, it increased funding for its Bard and Gemini AI projects.
Canva quietly removed several technical writing roles, as its internal teams started using generative AI to create documentation. This was reported by the Australian Financial Review.
Bumble announced it would lay off 30% of its global workforce as part of a major restructuring effort. The company said it is realigning toward AI-powered product development and operational efficiency.
Disney continued its global layoffs. Hundreds were let go across its marketing, tech, and Hulu divisions. While AI was not the only reason, the company stated it is modernizing workflows using automation
May
IBM laid off around 8,000 employees, mainly from its HR department. These roles were replaced by an internal AI chatbot called AskHR. The company said it is hiring software engineers and data analysts instead.
Microsoft let go of 6,500 employees, primarily from legal, engineering, and product management roles. CEO Satya Nadella said AI tools like GitHub Copilot are now writing up to 30 percent of new code, reducing the need for layers of support teams.
Amazon CEO Andy Jassy told staff that the company expects to need fewer people in many roles due to the rollout of AI agents. He said in an internal memo that AI will reduce the size of the corporate workforce over time.
Chegg, the education tech company, cut 240 jobs, about 22 percent of its staff. The reason: many students are now using free tools like ChatGPT instead of Chegg’s paid study services.
April
Intel made headlines by announcing plans to cut over 21,000 jobs. That is nearly 20 percent of its total workforce. The company is shifting focus toward AI chip manufacturing and shutting down parts of its autonomous driving division.
Google laid off several hundred employees in its platforms and devices group. At the same time, it announced additional investment in its AI research division.
Meta reduced headcount in its Reality Labs division. The company is pulling back from its metaverse ambitions and focusing more on AI-powered features across Instagram and Facebook.
March
Cybersecurity firm CrowdStrike let go of 5 percent of its workforce. The CEO explained in a interview that AI is allowing the company to move faster and become more efficient, requiring fewer people.
Block, formerly known as Square, laid off nearly 1,000 employees. The CEO said this was not about AI directly, but about company structure. However, analysts believe automation played a quiet role in the decision.
February
Meta laid off nearly 4,000 people, saying the company wants to "move out low performers" and shift its focus to hiring machine learning engineers.
Workday and Salesforce also cut hundreds of roles while increasing hiring in AI-focused departments. Both companies said they are adapting their roadmaps to put AI at the center of their enterprise tools.
January
The year started with smaller cuts. Microsoft removed a small number of roles in its gaming and sales departments. These were called "performance-based" exits. But it became clear over the following months that these were part of a bigger trend toward restructuring for AI.
How AI Is Driving These Changes?
Companies are using AI to either reduce their workforce or make existing teams more productive. Here are the three main patterns we are seeing:
1. AI is replacing repetitive jobs
At IBM, internal tools like AskHR have taken over most basic HR functions. Canva no longer needs as many technical writers. CrowdStrike’s CEO said AI lets them move from idea to product faster with fewer people.
2. Companies are cutting teams to fund AI growth
Meta, Salesforce, and Google have all redirected budgets from traditional product lines to AI infrastructure, model training, and hiring. Microsoft has publicly said it wants a flatter team structure with more engineers and fewer middle managers.
3. AI is helping people do more with less
At Microsoft, tools like Copilot assist with code generation. Developers are producing more work in less time. That means companies need fewer people per project.
The roles disappearing first
1. Software engineering
Microsoft reports that 40 percent of its recent layoffs affected developers. AI tools now perform many of the tasks previously done by junior programmers.
2. Human resources
IBM eliminated thousands of HR positions in one move and does not plan to rehire.
3. Customer support
Chegg said that users prefer automated help over human agents. Many support roles are now considered nonessential.
4. Content creation
More than 80 percent of marketing leaders report using AI to create written content. AI-generated writing is often considered good enough to replace human-created work.
5. Data analysis
AI systems are being used to review financial and business data at speeds and scale human analysts cannot match.
6. Middle management
Companies like Intel are removing multiple layers of team leadership, saying they can now track performance and coordinate work using automation tools.
What CEOs Are Actually Saying?
Microsoft CEO Satya Nadella:"The clear focus as a company is to define the AI wave and empower all our customers to succeed in the adoption of this transformative technology."
Meta CEO Mark Zuckerberg:
"This is going to be an intense year, and I want to make sure we have the best people on our teams... AI, glasses as the next computing platform and the future of social media."
Amazon CEO Andy Jassy:"As we roll out more Generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today."
They're not hiding it. They're telling you AI will replace human workers.
Where the impact is being felt?
California
Over 11,000 jobs have been lost in the Silicon Valley region alone.
Washington State
More than 2,300 positions have been eliminated at Microsoft’s headquarters and surrounding offices.
Texas
Tesla’s Austin operations were reduced by over 14,000 workers.
India
Major tech hubs in cities like Bangalore have seen staff reductions across customer service and operational roles.
Why this is different from previous downturns?
These layoffs are not being driven by financial crisis. Microsoft, Amazon, and others are posting strong earnings. In the first quarter of 2025, Microsoft reported revenue of 70.1 billion dollars, a 13 percent increase from the same time last year.
At the same time, the company cut more than 15,000 jobs.
AI adoption is now a business strategy, not a side project. Companies are showing that they can grow while reducing staff. They are not planning to bring these roles back.
What workers can do?
Professionals in the tech industry must now decide how to adapt. The safest option is to develop skills that AI cannot replicate. These include strategic thinking, interpersonal communication, complex decision-making, and the ability to lead or supervise mixed AI-human teams.
Learning how to use AI effectively is also essential. Workers who understand these systems and can use them to their advantage are more likely to stay relevant.
Conclusion
The first half of 2025 has confirmed what many experts predicted. Artificial intelligence is not simply enhancing work. It is replacing workers.
Companies are not slowing down their AI investments. They are accelerating them. Budgets are being redirected away from human resources and toward machine intelligence.
This isn't temporary. Companies aren't planning to hire these people back. They're using the money they save to buy more AI systems.
You have two choices: Learn to work with AI or get replaced by it. The companies have already decided. Now you need to decide what you're going to do about it.
| 2022-12-01T00:00:00 |
https://www.finalroundai.com/blog/ai-tech-layoffs-mid-2025
|
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|
What is the real explanation behind 15000 layoffs at Microsoft?
|
The heart of the internet
|
https://www.reddit.com
|
[] |
Between May and now Microsoft laid off 15,000 employees, stating, mainly, that the focus now is on AI. Some skeptics I've been talking to are ...
|
I need help understanding this article on Inc.
https://www.inc.com/jason-aten/microsofts-xbox-ceo-just-explained-why-the-company-is-laying-off-9000-people-its-not-great/91209841
Between May and now Microsoft laid off 15,000 employees, stating, mainly, that the focus now is on AI. Some skeptics I’ve been talking to are telling me that this is just an excuse, that the layoffs are simply Microsoft hiding other reasons behind “AI First”. Can this be true? Can Microsoft be, say, having revenue/financial problems and is trying to disguise those behind the “AI First” discourse?
Are they outsourcing heavily? Or is it true that AI is taking over those 15,000 jobs? The Xbox business must demand a lot and a lot of programming (as must also be the case with most of Microsoft businesses. Are those programming and software design/engineering jobs being taken over by AI?
What I can’t fathom is the possibility that there were 15,000 redundant jobs at the company and that they are now directing the money for those paychecks to pay for AI infrastructure and won’t feel the loss of thee productivity those 15,00 jobs brought to the table unless someone (or something) else is doing it.
Any Microsoft people here can explain, please?
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/ArtificialInteligence/comments/1lsxtk0/what_is_the_real_explanation_behind_15000_layoffs/
|
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|
Microsoft to cut up to 9,000 jobs as it invests in AI - BBC
|
Microsoft to cut up to 9,000 jobs as it invests in AI
|
https://www.bbc.com
|
[] |
The US tech giant will axe 4% of its global workforce and plough money into artificial intelligence.
|
Microsoft to cut up to 9,000 more jobs as it invests in AI
3 July 2025 Share Save Lily Jamali • @lilyjamali North America Technology Correspondent Reporting from San Francisco Share Save
Reuters
Microsoft has confirmed that it will lay off as many as 9,000 workers, in the technology giant's latest wave of job cuts this year. The company said several divisions would be affected without specifying which ones but reports suggest that its Xbox video gaming unit will be hit. Microsoft has set out plans to invest heavily in artificial intelligence (AI), and is spending $80bn (£68.6bn) in huge data centres to train AI models. A spokesperson for the firm told the BBC: "We continue to implement organisational changes necessary to best position the company for success in a dynamic marketplace."
The cuts would equate to 4% of Microsoft's 228,000-strong global workforce. Some video game projects have reportedly been affected by them. According to an internal email seen by The Verge and gaming publication IGN, Microsoft has told gaming staff that the planned reboot of first-person shooter series Perfect Dark, along with another title, Everwild, will be cancelled. The Initiative, a Microsoft-owned studio behind the Perfect Dark reboot, will also be shut down, the memo stated. Job cuts have also affected staff across wider studios owned by Microsoft, including Forza Motorsport maker Turn 10 and Elder Scrolls Online developer ZeniMax Online Studios, according to employee posts on social media seen by the BBC. Matt Firor, studio director of ZeniMax Online Studios, announced on Wednesday he would be leaving his position in July after more than 18 years at the studio. "While I won't be working on the game anymore, I will be cheering you on and adding to the thousands of hours I've already spent in-game," said a post attributed to Mr Firor by ZeniMax on X. Romero Games Ltd, an independent studio based in Galway, Ireland - co-founded by Doom developer John Romero - has also cut staff after funding for its game was axed by its publisher. "These people are the best people I've ever worked with, and I'm sorry to say that our game and our studio were also affected," said Mr Romero in a post on X. Microsoft has initiated three other rounds of redundancies so far in 2025, including in May when it said it would cut 6,000 roles. An official database maintained by Washington state shows that more than 800 of the positions eliminated will be concentrated in the city of Redmond as well as in Bellevue, another Microsoft hub in its home state.
AI refocus
| 2022-12-01T00:00:00 |
https://www.bbc.com/news/articles/cdxl0w1w394o
|
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|
A.I. Drives Job Cuts Across Silicon Valley Giants: By the Numbers
|
A.I. Drives Job Cuts Across Silicon Valley Giants: By the Numbers
|
https://observer.com
|
[
"Alexandra Tremayne-Pengelly",
"Alexandra Tremayne-Pengelly Covers General News",
"The Business Of Arts",
"Culture For The Observer. Previously",
"She Wrote For Magazine",
"Volunteered At The Inmate-Run Newspaper San Quentin News. She Is A Graduate Of Mcgill University",
"The Columbia School Of Journalism. Follow Her On Twitter"
] |
So far in 2025, nearly 400 tech companies have announced layoffs, affecting close to 94,000 employees, according to TrueUp's tech layoff tracker ...
|
Tech layoffs are nothing new in Silicon Valley, a region long shaped by boom-and-bust hiring cycles, speculative investment and shifting economic conditions. But in recent months, executives have offered a different explanation for job cuts: the rapid rise of A.I. So far in 2025, nearly 400 tech companies have announced layoffs, affecting close to 94,000 employees, according to TrueUp’s tech layoff tracker. Many of these roles are expected to be replaced—directly or indirectly—by A.I.-driven efficiencies.
Sign Up For Our Daily Newsletter Sign Up Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. See all of our newsletters
Salesforce, for example, cut 1,000 roles earlier this year, redirecting hiring toward sales roles focused on A.I.-powered products. CEO Marc Benioff said last month that A.I. currently handles 30 to 50 percent of the company’s work, reducing the need for roles in fields like software engineering and customer support. Despite the job cuts, Salesforce remains financially strong, reporting $9.8 billion in revenue for the February-April quarter, an 8 percent year-over-year increase.
Microsoft, the world’s second largest company by market capitalization, has also made significant cuts in 2025. The company laid off around 9,000 employees in July, following a separate round of more than 6,000 in May. Software engineers have borne the brunt of these reductions.
Though Microsoft has not explicitly linked the layoffs to A.I., the technology’s growing role inside the company is undeniable. In April, CEO Satya Nadella disclosed that A.I. now writes about 30 percent of Microsoft’s code—a figure he expects to climb.
Microsoft isn’t alone in its internal shift toward A.I. At Google, well over 30 percent of new code includes A.I. generated suggestions, CEO Sundar Pichai revealed earlier this year. At Meta, Mark Zuckerberg has described developing an A.I. agent with coding abilities comparable to a mid-level engineer as one of the company’s top goals for 2025.
Startups across Silicon Valley are following suit, increasingly requiring A.I. fluency in hiring and day-to-day operations. In a March memo to staff, Shopify CEO Tobias Lütke wrote that A.I. use is now “a fundamental expectation,” adding that any team requesting additional headcount or resources must first prove that the task can’t be handled by A.I.
Duolingo CEO Luis von Ahn echoed a similar stance the following month. He told employees that headcount increases would only be approved if teams demonstrate the need for human involvement over automation. A.I. proficiency, he added, will also play a bigger role in hiring and performance reviews. On top of that, Duolingo will stop using contractors for tasks that A.I. can complete.
With 2025 only halfway through, A.I.-driven workforce changes are poised to continue. In a June memo, Amazon CEO Andy Jassy laid out the company’s sweeping integration of A.I. across areas like shopping, AWS, and internal operations. “It’s hard to know exactly where this nets out over time,” he wrote, “but in the next few years, we expect this will reduce our corporate workforce as we gain efficiency from using A.I. extensively across the company.”
| 2025-07-08T00:00:00 |
2025/07/08
|
https://observer.com/2025/07/ai-fueled-layoffs-creep-across-silicon-valley/
|
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] |
Why so many people fear AI at work: risks, layoffs, and job security
|
Why so many people fear AI at work: risks, layoffs, and job security
|
https://www.datastudios.org
|
[] |
The overlap between AI capability and real work tasks is not speculative: it is now measured, mapped, and visible in the numbers.
|
The overlap between AI capability and real work tasks is not speculative: it is now measured, mapped, and visible in the numbers.
A new OECD meta-study published just days ago makes it clear that the apprehension so widely felt is not the result of media panic or baseless rumor, but instead arises from a cold, statistical reality: nearly 40% of global jobs today—across every sector, but particularly in advanced economies where the content of work is more cognitive than manual—now involve tasks that generative AI can already perform at a high level. And the percentage rises to about 60% in the economies where office-based, information-driven work is the norm and the threshold for routine is defined by what can be done with data, text, or code. This is not simply a matter of warehouse robots replacing lift drivers; it is a wave of substitution for the very tasks that used to define professional “knowledge work.”
The World Economic Forum’s Future of Jobs 2025 survey, which collects projections from over a thousand multinationals, now expects that automation, and particularly generative models, will not only create jobs—around 14% of today’s employment is forecast to come from roles that barely exist yet—but will also displace the equivalent of 8% of the current workforce (nearly 92 million jobs) between 2025 and 2030. The most significant transformation, however, is not just in headcount but in the content of roles: WEF expects that 39% of all skills in every job will be outdated within this window, requiring mass reskilling on a scale and with a speed that is unprecedented in labor history. It is these hard numbers, and not simply headlines, that make anxiety rational and pervasive.
Real companies are translating forecasts into payroll cuts, not just process diagrams.
It is not a theoretical risk when multinationals and tech-forward firms begin making structural changes to their teams, guided explicitly by AI adoption. The Dutch navigation firm TomTom eliminated 300 employees on June 30, 2025, folding an entire “application layer” team into a much smaller, AI-centric product group, demonstrating that entire organizational layers can become redundant when new tooling makes old workflows obsolete.
Klarna—a prominent fintech platform—made international headlines by loudly advertising that its GPT-powered assistant “now does the work of 700 agents.” While a month later the company admitted that customer experience metrics declined and it began rehiring human support for specific cases, the net effect is still a 40% reduction in team size, with no intent to return to pre-AI staffing levels.
At IBM, the CEO confirmed in May that “several hundred” roles in human resources—traditionally considered protected, complex, and high-trust—have already been handed to AI systems, with freed budgets redirected to core engineering. The message to every office worker is stark: if the role’s main activities involve repeatable decisions, standardized documents, or structured communication, it is now exposed.
These are not pilot projects or minor tweaks—they are visible, irreversible changes to the corporate headcount, and their example resonates through the entire labor market.
Generative models do not just automate tasks; they erode the “task-core” of many established professions.
Whereas the previous generations of workplace automation targeted physical activity—on the assembly line, in warehouses, in logistics—today’s leading AI models are directly attacking the core activities of traditional white-collar employment: parsing and generating language, drafting or analyzing code, producing reports and presentations, summarizing contracts, and even assisting in design or research.
The latest McKinsey workplace analysis concludes that up to 30% of the average knowledge worker’s week—activities such as building first-draft slides, writing emails, reconciling figures, or basic customer outreach—can already be offloaded to today’s foundation models. And in organizations that move fastest, this does not simply create time for workers to “move up the value chain”; instead, it allows companies to consolidate entire workflows, retaining a smaller group of higher-skill staff while disbanding teams whose primary function has become automatable. The process is gradual and often invisible, happening task by task rather than as a single dramatic layoff, which means that many employees only realize the vulnerability of their job once the very last manual or creative task is automated away.
Workers are not just guessing: they feel, with acute specificity, that AI will erode their prospects and stability.
A February Pew survey of U.S. employees shows the depth of concern with new clarity: 52% of workers now say they are “worried” about AI’s impact on their own work life, and 33% feel “overwhelmed” rather than optimistic or excited. Critically, only 36% report feeling “hopeful.” Among clerical staff, accounting teams, and junior analysts—the precise segments that both the WEF and McKinsey flag as most exposed—the sense of threat is even sharper, with a full 32% of respondents already expecting personally “fewer opportunities” as a result of AI in their sector.
This is not a vague or general concern: it is rooted in the experience of seeing colleagues redeployed, laid off, or reassigned as AI-driven process redesigns trickle through organizations in real time.
The bottleneck in reskilling: a structural gap that will leave millions exposed.
While every optimistic projection from international agencies now insists that reskilling is the answer, the data reveal a grim gap between aspiration and execution. The WEF’s own numbers show that 59% of workers worldwide will require significant training by 2030 to keep pace with changing roles, but that employers themselves believe at least 11% of those affected will receive no reskilling at all—either because programs do not exist, or because the economic calculus favors replacement over retraining.
OECD analysis indicates that today, only 1% of roles are fully automatable, but as organizations move beyond simply adding AI tools “on top” and start to reengineer entire workflows, exposure spikes: more and more jobs will require workers to reinvent their skills before—not after—they become redundant. This demands a timeline for change that is not always matched by corporate budgets or by the willingness of individuals to invest in learning new, often unrelated, abilities at mid-career.
Inequality is poised to worsen: AI’s wage impact runs in two directions, not one.
It is not just the number of jobs at stake, but the distribution of rewards and security. An IMF working paper on the global diffusion of AI finds that, at current rates, early adoption disproportionately increases earnings for those with high-skill, AI-complementary roles, while compressing or eroding wages for those in mid-tier, routine jobs, and doing little to assist the lowest-skilled. The net effect is to widen the income gap (as measured by the Gini coefficient), unless tax, welfare, and training policies intervene. So even in advanced economies where overall employment may not decline, the sense of relative insecurity grows sharper: not only might your job go, but the rungs of the social ladder may get further apart.
Institutions are slow, but technology moves on its own curve: the “asymmetric risk” of delayed regulation and support.
Whereas corporate adoption of generative AI can be measured in quarters—firms deploy cloud-based LLM APIs and change workflows in months—statutory severance, reskilling subsidies, and collective-bargaining agreements typically take years to adapt. The lag between a CTO’s procurement and a parliament’s policy cycle is now a central source of fear: workers face what sociologists call “asymmetric risk,” where the forces that threaten job security act faster than the forces meant to provide stability, fairness, or second chances. For every worker who loses a job because of AI-driven restructuring, there are thousands more who see the headlines and realize there is no quick path back to stability.
The psychological dimension: identity and status are harder to reinvent than skill-sets.
Economic models often count only dollars or roles, but surveys and qualitative research show that for many employees—particularly those with years or decades invested in a profession—what is most devastating is not simply a change in daily activity, but the sudden sense that their skills, training, and status are obsolete, exchangeable, or devalued. As news cycles publicize the replacement of engineers, marketers, or even creative professionals by AI, the implicit promise that higher education and professional commitment guarantee long-term stability is eroded. The loss is not just financial, but existential: work has always been a source of purpose, connection, and recognition. That foundation now seems provisionally, and perhaps permanently, unstable.
Data and detail underpin the fear: measurable exposure, real-world layoffs, and institutional lag combine to make anxiety rational.
The current wave of AI-driven change is not a story of distant science fiction or panic-stoking headlines. It is a process that can now be tracked in data—OECD, WEF, Pew, McKinsey, IMF—and in the decisions and public statements of major employers across continents. Millions of jobs will change, millions will be replaced, and many millions more will be exposed to new forms of uncertainty as both technology and society adapt, at mismatched speeds, to a world where “work” itself is being reinvented from the ground up.
______
FOLLOW US FOR MORE.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.datastudios.org/post/why-so-many-people-fear-ai-at-work-risks-layoffs-and-job-security
|
[
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] |
Microsoft to replace salespeople with AI solutions engineers
|
Microsoft is replacing salespeople with "solutions engineers" amid recent layoffs — promoting Copilot AI while ChatGPT dominates the enterprise sector
|
https://www.windowscentral.com
|
[
"Kevin Okemwa",
"Social Links Navigation"
] |
The recent layoffs seem to be part of Microsoft's strategy to improve its sales of AI tools metrics, with customers requesting technical ...
|
The recent layoffs seem to be part of Microsoft's strategy to improve its sales of AI tools metrics, with customers requesting technical support and demos.
Earlier this week, Microsoft laid off 9,000 employees just after it ended its fiscal year, which translates to about 4% of its workforce. The layoffs impacted most of the company's divisions, including Xbox and Azure:
"We continue to implement organizational and workforce changes that are necessary to position the company and teams for success in a dynamic marketplace."
As it turns out, the layoffs seem to be part of Microsoft's strategy to improve its sales of AI tools metrics. According to Business Insider, the vast majority of employees affected by the recent layoffs were salespeople.
The report further suggests that Microsoft intends to replace the affected employees with more technical salespeople to boost the sale of its AI tools as it tries to play catch up with Google and OpenAI.
This news comes after a separate report suggested that Microsoft often pesters OpenAI, requesting help to spell out the inner workings of its tech to employees. A source disclosed that Microsoft doesn't have the technical know-how to fully leverage its IP rights and often doesn't know what questions to ask.
As such, Microsoft plans to leverage these technical salespeople (often referred to as "solution engineers" internally) to better present products to customers, incorporating demos earlier on in the sales process.
So, Microsoft is reportedly using this approach to bolster AI sales by prompting technical and industry understanding among its customers. However, the company is also set to hire more salespeople, which it plans to use beyond its headquarters.
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A person with close affiliations to Microsoft revealed that feedback from customers indicated that the sales process was a tad daunting and took too long before getting to the demos and technical aspects of the sales process:
"The customer wants Microsoft to bring their technical people in front of them quickly. We need someone who is more technical, much earlier in the cycle."
Interestingly, a separate report suggests that Microsoft struggles with Copilot sales, predominantly because most corporations prefer OpenAI's ChatGPT.
As you may know, Microsoft is doubling down on its AI efforts despite the gigantic rift forming between its partnership with OpenAI over its time-sensitive, for-profit evolution plans.
In an email sent to the company's top management, Microsoft's President of Developer Division and GitHub, Julia Liuson, pushed for the broad adoption of internal AI tools like GitHub Copilot across the company. She further revealed that it would be used as a metric during performance evaluations:
"AI is now a fundamental part of how we work. Just like collaboration, data-driven thinking, and effective communication, using AI is no longer optional — it's core to every role and every level."
This isn't the first time the tech giant has been impacted by massive layoffs this year. It started off the year on a bumpy ride by announcing performance-based job cuts across several departments, including security, slated to impact "less than 1%" of the workforce:
“At Microsoft we focus on high-performance talent. We are always working on helping people learn and grow. When people are not performing, we take the appropriate action.”
Shortly after, there was another round of layoffs, this time impacting employees across security, experiences, sales, devices, and gaming departments. Later that month, the Redmond giant announced a hiring freeze, which impacted its consulting business in the U.S. as part of its broader plans to cut costs.
Microsoft CEO Satya Nadella is betting big on AI and is slated to continue heavily investing in landscape, including an $80 billion investment for building data centers to meet the company's cloud computing needs. The company has heavily integrated AI across its tech stack to drive more interest and leverage its dominant Windows market share to gain broad adoption. This is despite mounting profitability concerns among investors and its reported internal struggles with Copilot and AI.
| 2025-07-04T00:00:00 |
2025/07/04
|
https://www.windowscentral.com/microsoft/microsoft-replacing-salespeople-with-solutions-engineers
|
[
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},
{
"date": "2024/06/01",
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}
] |
After 9,000 Layoffs, Microsoft Boss Has Brutal Advice for Sacked ...
|
After 9,000 Layoffs, Microsoft Boss Has Brutal Advice for Sacked Workers
|
https://futurism.com
|
[] |
Microsoft has laid off about 9,000 workers in the midst of a newly-announced $80 billion AI investment — and apparently, those who just lost ...
|
Microsoft has laid off about 9,000 workers in the midst of a newly-announced $80 billion AI investment — and apparently, those who just lost their jobs should be talking to ChatGPT about it.
As Aftermath reports, an executive producer at Microsoft-owned Xbox ended up with egg on his face after suggesting that laid off workers pour their hearts out to AI.
"These are really challenging times, and if you’re navigating a layoff or even quietly preparing for one, you’re not alone and you don’t have to go it alone," that producer, Matt Turnbull, said in a since-deleted LinkedIn post that Aftermath thankfully screenshotted for posterity. "No AI tool is a replacement for your voice or your lived experience. But at a time when mental energy is scarce, these tools can help get you unstuck faster, calmer, and with more clarity."
"I know these types of tools engender strong feelings in people, but l'd be remiss in not trying to offer the best advice I can under the circumstances," he continued. "I've been experimenting with ways to use [large language model] Al tools (like ChatGPT or Copilot) to help reduce the emotional and cognitive load that comes with job loss."
Yes, you read that right: a Microsoft boss was telling those just laid off by the tech giant that they should use chatbots — run or funded by the company that just fired them — to avoid crying on a company shoulder.
Following that phoned-in introduction, Turnbull offered a few potential prompts for AI as a job loss grief counselor, including those that help with career planning, resume-building, networking, and, our personal favorite, "emotional clarity [and] confidence."
"I'm struggling with imposter syndrome after being laid off," Turnbull's "clarity" prompt reads. "Can you help me reframe this experience in a way that reminds me what I'm good at?"
It comes as little surprise, given how absolutely tone-deaf those suggestions are, that folks on social media had quite a lot to say to the Xbox executive.
"The new Severance season is insanely good," joked one commentator on X-formerly-Twitter.
As another irked observer wrote on the r/gaming subreddit, "anyone that tells people who were fired to talk to a computer chat algorithm for therapy is insane."
Indeed, gamers seem to be the most affronted by Turnbull's attempt at sensitivity and advice, with another X commentator remarking that his response to those layoffs was one of "the most tone-deaf and cruelest things" they'd ever seen.
"I hope this finally shatters the illusion for some people that Xbox is not your good buddy," that same user quipped.
Though it's hard to say whether the Xbox producer's sentiments were sincere or not, it's clear from the subsequent deletion of the post that he was made to feel some type of way about it after putting it out into the world — and honestly, that potential embarrassment is the most we can hope for with these sorts of tech bros.
More on AI: Journalists Just Roasted Sam Altman To His Face
| 2022-12-01T00:00:00 |
https://futurism.com/microsoft-boss-ai-advice
|
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{
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|
More Tech Layoffs, The Decline of SaaS, and a Guide to Einstein ...
|
More Tech Layoffs, The Decline of SaaS, and a Guide to Einstein Activity Capture
|
https://www.salesforceben.com
|
[
"Sasha Semjonova"
] |
The AI-centered layoffs trend continues, with IT leader Microsoft revealing that it will be laying off up to 9,000 workers in its second wave of ...
|
Here’s our rundown of last week’s top stories. Want this content delivered straight to your inbox? Sign up now!
Ultimate Guide to Einstein Activity Capture: Sync Email as Activity and More
Ever heard of Einstein Activity Capture? If not, today is your lucky day…
Einstein Activity Capture (EAC) is Salesforce’s tool for automatically syncing emails and calendar events with Salesforce, helping users keep their activity data up to date without manual entry. It works by connecting Salesforce with Google or Microsoft accounts, capturing emails and events related to Salesforce records, and displaying them in the activity timeline. It’s not a perfect replacement for traditional activity logging, but it works pretty well!
Why should you care now? Well, Salesforce has introduced features like Enhanced Email and the ability to convert EAC-captured emails into permanent activities via the “Sync Email as Activity” option. This gives teams more control over what’s logged in the system and supports better visibility and reporting, and you can get started with this today.
Check out the original post here.
SaaS Is Still On the Slowdown: What This Means for Salesforce
Once upon a time, the Software as a Service (SaaS) industry was booming, transforming the way businesses handled their data, services, and workflows. Now, 25 years on, SaaS is firmly past its glory days.
In June, Altimeter Capital’s Jamin Ball reported a 29% YoY drop in the SaaS market for Q1 2025 – the cloud software sector’s worst quarterly performance in years. A number of factors have been influencing this decline, including the fact that the market is saturated with options, companies are being pickier with their software selections, and the majority of incremental budgets are being spent on artificial intelligence.
If SaaS businesses want to get ahead, they will need to adapt. Prioritizing mission-critical use cases, delivering excellent customer service, and working with AI – not against it – will be critical.
Read the full post here.
75% of Salesforce Admins Are Getting AI-Certified – Here’s Why
The news is in: Salesforce Admins – 75% of them, in fact – are getting AI-certified. But why?
According to Salesforce Ben’s latest Salesforce Administrator Survey, over 75% of admins are actively pursuing or have already earned AI certifications. This surge reflects the growing interest in staying competitive as AI tools like Einstein and Agentforce become more embedded in the platform.
Admins are recognizing that AI skills aren’t just nice to have – they’re quickly becoming essential for managing smarter automations and staying ahead in an evolving job market.
While many admins are optimistic about AI’s potential to boost productivity and simplify processes, there’s also caution. Still, the majority see AI as an opportunity rather than a threat – something that can enhance their role rather than replace it.
Check out the original post here.
How True Are Marc Benioff’s AI Implementation Claims?
Last week, Salesforce CEO Marc Benioff made headlines when he claimed that artificial intelligence now handles between 30–50% of all internal work at the company. You can say that caused some commotion.
In an interview with Bloomberg, Benioff revealed that the company had reported productivity gains of 30-50% with the use of AI, alluding to the fact that a high percentage of internal work was being completed with AI. This comes after Salesforce’s mass layoffs in February this year, where it was revealed that the cloud giant was cutting more than 1,000 jobs in order to make room for more AI-focused roles.
Benioff has also come under fire over speculation that his claims just aren’t true and all a ruse to further market Salesforce’s proprietary AI, Agentforce.
Read the full post here.
More AI-Centered Layoffs? Microsoft to Cut Up to 9,000 Workers
The AI-centered layoffs trend continues, with IT leader Microsoft revealing that it will be laying off up to 9,000 workers in its second wave of mass layoffs this year.
The layoffs will affect multiple teams, geographies, and tenures, and are happening in an effort to streamline processes and reduce layers of management. Perhaps unsurprisingly, they have also been linked to increased AI spending, especially as Microsoft announced that it would be spending $80B on AI data centers.
Microsoft had 228,000 workers at the end of June 2024, 45,000 of them in sales and marketing. It’s clear that these layoffs – as well as the other 63,800 tech layoffs that have taken place this year – are significant, and these are likely not the last of the year.
Read the full post here.
10 Things Salesforce Admins Need to Know About Apex
Think Salesforce Apex is just a developer’s tool? Well, you might need to think again…
Apex is more than just a coding language – it’s a great skill to have under your belt, and a great technology to understand, harness, and make the most out of, whether you’re an admin, developer, or architect.
That’s why we have put together a guide featuring 10 things that Salesforce Admins need to know about Apex, including how it can help you and your day-to-day workflows, as well as how it can help you become a better (and more efficient) colleague, especially to developers.
Watch the video here.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.salesforceben.com/more-tech-layoffs-the-decline-of-saas-and-a-guide-to-einstein-activity-capture/
|
[
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"date": "2022/12/01",
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"query": "artificial intelligence layoffs"
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Xbox Producer Offers Unique Solution to Laid-off Microsoft Employees
|
Xbox Producer Offers Unique Solution to Laid-off Microsoft Employees: ChatGPT Copilot Prompts
|
https://opentools.ai
|
[] |
These AI-driven assistants are particularly beneficial for professionals facing layoffs or career shifts. ... Artificial Intelligence (AI) tools ...
|
Introduction to the News Article
In recent developments, the tech industry has been abuzz with conversations around workforce changes and the integration of advanced technologies. A noteworthy incident involves the Xbox producer at Microsoft who provided a unique perspective to former employees affected by layoffs. Highlighting the potential of utilizing innovative tools, the producer recommended the use of ChatGPT and Copilot as prompts, suggesting that such technologies can serve as invaluable resources for those navigating career transitions. This move not only underscores the increasing reliance on artificial intelligence but also reflects a progressive mindset aimed at empowering individuals in challenging times. For more insights into this advice, the full article can be accessed here.
| 2022-12-01T00:00:00 |
https://opentools.ai/news/xbox-producer-offers-unique-solution-to-laid-off-microsoft-employees-chatgpt-copilot-prompts
|
[
{
"date": "2022/12/01",
"position": 52,
"query": "artificial intelligence layoffs"
}
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|
CEOs Say AI Is Poised to Wipe Out an Astonishing Number of Jobs
|
CEOs Say AI Is Poised to Wipe Out an Astonishing Number of Jobs
|
https://futurism.com
|
[] |
Billionaire tech moguls aren't the only ones doomsaying about artificial intelligence layoffs. CEOs across a range of industries are now ...
|
Billionaire tech moguls aren't the only ones doomsaying about artificial intelligence layoffs. CEOs across a range of industries are now jumping on the bandwagon, saying it's no longer a matter of "if," but "how many" jobs AI will take.
A recent survey by the Wall Street Journal explored just how pervasive the automation idea is throughout a number of industries, and execs aren't pulling any punches.
For example, CEO of Ford Motor Company Jim Farley recently predicted that AI "is going to replace literally half of all white-collar workers in the US." He added that "AI will leave a lot of white-collar people behind."
In June, Amazon CEO Andy Jassy spooked his employees with a memo saying to expect layoffs in the next few years because of the "once-in-a-lifetime" revolution of AI.
And at JPMorgan Chase, CEO Marianne Lake recently told investors to expect the company's overall head count — and therefore its payroll expenses — to fall by as much as 10 percent over the next few years, thanks to the magic of AI.
If the AI automation dystopia really is upon us, it evidently slipped under the radar of the US Bureau of Labor Statistics, which recently released its latest jobs report. Among other things, it found that the US added 147,000 jobs in June, slightly dropping the unemployment rate from 4.2 percent to 4.1 — and seemingly refuting the claim of an impending AI takeover, at least for now.
Over half of these jobs, NBC notes, were in state and local government roles, while healthcare, social, service, and construction work made up the bulk of other gains.
Still, there are some major issues buried in the data, like the fact that long-term unemployment — people unemployed for six months or more — has skyrocketed from 190,000 to a whopping 1.6 million. Meanwhile, the number of people unemployed at the median length of unemployment, 15 weeks or more, jumped from 34.9 percent to 38.3 percent. That level hasn't been seen since the throes of the pandemic, NBC notes.
While those numbers are bad news — like, really bad — the crisis they point to is a little more complicated than an AI-powered dystopia. At their core, these numbers are arguably the result of our economy's dependence on unemployment laundered through AI hype.
As economic researchers Jeffrey Funk and Gary Smith observed in a recent column, revenue from large language model (LLM) adoption is falling far short of the tech industry's promises. What gets presented as proof of AI's automation potential is instead a mash of penny-pinching layoffs, outsourcing, labor market saturation, and in some cases, employer bias against recent college grads.
With that view in mind, all the CEO bluster looks less like an imminent crisis brought on by AI, and more like business as usual in a market economy. What workers do about that whole situation is another story altogether.
More on Automation: Professors Staffed a Fake Company Entirely With AI Agents, and You'll Never Guess What Happened
| 2022-12-01T00:00:00 |
https://futurism.com/ceos-ai-job-market
|
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|
AI Triggers Unprecedented Layoffs at Top Tech Firms - LinkedIn
|
AI Triggers Unprecedented Layoffs at Top Tech Firms
|
https://www.linkedin.com
|
[
"Alexander Böhns",
"Experienced International Technology Advisor",
"Expertise In It",
"Sales",
"Consulting",
"Strategic Thinker",
"Effective Communicator",
"Multicultural Collaboration",
"Kate Brown",
"Chief Marketing Officer At Insurely Inc."
] |
Microsoft is currently leading the largest AI-driven workforce layoffs in 2025, having announced cuts of up to 9000 jobs in July alone—its ...
|
Microsoft is currently leading the largest AI-driven workforce layoffs in 2025, having announced cuts of up to 9,000 jobs in July alone—its biggest round since 2023. These layoffs represent about 4% of Microsoft's global workforce and are directly linked to the company's aggressive investment in artificial intelligence, with AI now responsible for writing up to 30% of new code and automating many internal processes.
IBM also made significant AI-related cuts, laying off around 8,000 employees earlier in the year, primarily in its HR department, with many of those roles replaced by an internal AI chatbot.
Other notable companies making large AI-related workforce reductions in 2025 include:
Intel: Announced plans to cut over 21,000 jobs as it shifts focus toward AI chip manufacturing.
Bumble: Cut 30% of its global workforce as it pivots to AI-powered product development.
Meta, Google, Amazon, and Disney have each made thousands of cuts, often citing AI automation and restructuring as major factors.
While not all layoffs are solely due to AI, the trend among major tech firms is clear: AI adoption is a central driver of large-scale workforce reductions in 2025, with Microsoft and IBM leading in absolute numbers of affected employees.
#AIJobLosses #JobLosses #TechLayoffs2025 #FutureOfWork #JobMarket #AIJobLosses
| 2022-12-01T00:00:00 |
https://www.linkedin.com/pulse/ai-triggers-unprecedented-layoffs-top-tech-firms-john-english-geudf
|
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|
Microsoft Producer Suggests Using AI To Ease Layoff Pain | Silicon
|
Microsoft Producer Suggests Using AI To Ease Layoff Pain
|
https://www.silicon.co.uk
|
[
"Matthew Broersma",
"Tom Jowitt"
] |
Microsoft Producer Suggests Using AI To Ease Layoff Pain · 'Strong feelings' · Job cuts · You might also like · Meta Cuts Staff In Oculus Studios ...
|
Getting your Trinity Audio player ready...
A Microsoft Xbox producer has drawn criticism by suggesting people who have been laid off in the company’s latest round of cuts use generative artificial intelligence to help deal with the emotional and practical impact of their situation.
Matt Turnbull, executive producer at Xbox Game Studios Publishing, in a now-deleted LinkedIn post said AI could help people deal with “challenging times”.
“If you’re navigating a layoff or even quietly preparing for one, you’re not alone and you don’t have to go it alone,” he wrote.
‘Strong feelings’
He acknowledged that generative AI tools create “strong feelings in people” but said he would be “remiss” if he didn’t try to offer “the best advice I can under the circumstances”.
“I’ve been experimenting with ways to use LLM Al tools (like ChatGPT or Copilot) to help reduce the emotional and cognitive load that comes with job loss,” Turnbull wrote.
Copilot is Microsoft’s generative AI brand, while ChatGPT developer OpenAI is minority owned by Microsoft.
Turnbull offered a series of AI prompts for career planning, writing a CV, job seeking and “emotional clarity and confidence”.
“These tools can help get you unstuck faster, calmer, and with more clarity,” he concluded.
“If this helps, feel free to share with others in your network. Stay kind, stay smart, stay connected.”
“You can almost see the thought process, ‘I need to do something to appear empathetic, but I also need to push Microsoft business interests,'” wrote one reader in response to the post, which was captured in a screenshot by tech news site Aftermath.
“Reads almost like what you’d get if you prompted ChatGPT: ‘Write a list of suggestions for recently laid off game studio employees to make it look like I care what happens to them while also subtly driving ChatGPT engagement,'” wrote another reader.
Job cuts
Silicon UK has contacted Microsoft for comment.
Turnbull wrote the post late last week after Microsoft said it would cut about 9,000 employees.
The cuts come at a time when Microsoft is investing heavily in AI infrastructure such as data centres, spending that has been eroding its profit margins.
A previous round of layoffs in May affected about 6,000 staff.
Microsoft has not specified which units will be affected by the most recent cuts, but reports suggest the company’s gaming business will be heavily hit, with two ambitious games being cancelled and at least one studio, The Initiative, being closed entirely.
The gaming division’s revenues rose by 8 percent year-over-year in Microsoft’s most recent quarter.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.silicon.co.uk/e-management/jobs/microsoft-ai-pain-620713
|
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AI job disruption accelerates with major layoffs across sectors, CEOs ...
|
AI job disruption accelerates with major layoffs across sectors, CEOs warn employees to adapt or lose out
|
https://www.cnbctv18.com
|
[] |
Job losses attributed to AI have already surged in 2025. Companies like Meta, Microsoft, IBM, Google, and Amazon have made cuts tied to AI- ...
|
The threat posed by artificial intelligence (AI) to white-collar jobs is intensifying, with a growing number of global CEOs warning employees to adapt or risk being left behind.Ford Motor CEO Jim Farley is the latest to raise the alarm, saying, “AI will leave a lot of white-collar people behind.” Farley predicts that 50% of white-collar jobs in America could be replaced by AI in the future.Marianne Lake, head of JPMorgan Chase’s consumer and community banking division, said in May that the bank expects to cut operations headcount by 10% due to AI. Around the same time, Dario Amodei, CEO of AI startup Anthropic, warned that half of all entry-level jobs could disappear within one to five years, potentially pushing US unemployment to 10–20%.Amazon CEO Andy Jassy also sounded a warning in June, calling AI a “once-in-a-lifetime” technology. “We will need fewer people doing some of the jobs that are being done today,” he said in a memo.The concern is not limited to the West. In February, Piyush Gupta, former CEO of DBS Bank, said that about 4,000 jobs may be lost in the next three to four years due to AI. In March, InMobi founder and CEO Naveen Tewari urged software engineers to adapt quickly: “Upgrade yourself, don’t ask me to upgrade you. Because this is survival. The world underneath you is shifting.”Job losses attributed to AI have already surged in 2025. Companies like Meta, Microsoft, IBM, Google, and Amazon have made cuts tied to AI-driven restructuring. Microsoft has laid off 15,000 employees in two major rounds this year. Business Insider slashed about 21% of its workforce in May. Procter & Gamble plans to eliminate around 7,000 white-collar jobs—15% of its non-manufacturing staff—over two years. InMobi is aiming for 80% automation in software coding by year-end, signaling more job losses ahead.According to venture capital firm SignalFire, entry-level hiring in major tech firms has dropped over 50% since 2019. Nvidia CEO Jensen Huang captured the emerging reality: “You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.”The layoffs are not solely the result of AI efficiencies. Many companies are actively cutting costs and reallocating capital toward AI development. The disruption is here—and it’s accelerating.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.cnbctv18.com/education/ai-impact-on-white-collar-jobs-unemployment-layoffs-19633785.htm
|
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The Human Side Of Human Resources Appears To Be Fading As 66 ...
|
The Human Side Of Human Resources Appears To Be Fading As 66% Of Managers Now Use AI To Make Layoff Decisions
|
https://allwork.space
|
[
"Allwork.Space News Team",
"The Allwork.Space News Team Is A Collective Of Experienced Journalists",
"Editors",
"Industry Analysts Dedicated To Covering The Ever-Evolving World Of Work. We Re Committed To Delivering Trusted",
"Independent Reporting On The Topics That Matter Most To Professionals Navigating Today S Changing Workplace",
"Including Remote Work",
"Flexible Offices",
"Coworking",
"Workplace Wellness",
"Sustainability"
] |
The poll of managers across various industries revealed that a majority are using AI tools like ChatGPT to weigh in on matters such as layoffs, ...
|
A new survey from ResumeBuilder.com shows a troubling change happening in workplace management: the increasing reliance on artificial intelligence, particularly large language models (LLMs), to guide major human resources decisions.
The poll of managers across various industries revealed that a majority are using AI tools like ChatGPT to weigh in on matters such as layoffs, promotions, and salary adjustments.
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There has been a sharp rise in AI’s role within HR operations. According to the data, 78% of respondents said they had consulted a chatbot when determining raises, while 77% did so for promotion decisions.
Even more strikingly, 66% acknowledged using an LLM to help decide which employees to let go. Nearly 20% of those surveyed admitted they often defer final decisions entirely to the AI, bypassing human judgment.
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ChatGPT is the most commonly used AI assistant among the tools mentioned, followed by Microsoft’s Copilot and Google’s Gemini. The enthusiasm for these tools highlights their perceived utility, but it also raises critical concerns about bias, reliability, and the diminishing role of human oversight in deeply personal workplace outcomes.
Experts have previously flagged issues with LLMs reinforcing user biases through overly agreeable or flattering responses, which is a problem that has affected ChatGPT in particular.
OpenAI has implemented updates to address these tendencies, but skepticism remains about the models’ ability to make sound, objective decisions in sensitive contexts.
Beyond the office, overreliance on AI has been linked to serious mental health issues among some users. The term “ChatGPT psychosis” has emerged to describe a detachment from reality reportedly experienced by individuals who excessively depend on the chatbot for advice and decision-making.
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In extreme cases, this dependence has been associated with job losses, relationship breakdowns, and psychiatric episodes requiring professional intervention.
As AI tools continue to gain traction in business environments, the findings serve as a cautionary tale about the potential risks of handing over high-stakes decisions to machines. While these technologies offer convenience and efficiency, their integration into matters of employment and personal well-being demands careful regulation and ethical scrutiny.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://allwork.space/2025/07/the-human-side-of-human-resources-appears-to-be-fading-as-66-of-managers-now-use-ai-to-make-layoff-decisions/
|
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},
{
"date": "2024/04/01",
"position": 89,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/06/01",
"position": 86,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 73,
"query": "artificial intelligence layoffs"
}
] |
AI Layoffs Begin: CEOs Admit White-Collar Jobs Will Suffer in Next 5 ...
|
AI Layoffs Begin: CEOs Admit White-Collar Jobs Will Suffer in Next 5 Years
|
https://www.businesstoday.in
|
[] |
Is your job safe from AI? Top CEOs and experts are finally speaking out — and the truth is chilling. Reports from the World Economic Forum, ...
|
Is your job safe from AI? Top CEOs and experts are finally speaking out — and the truth is chilling. Reports from the World Economic Forum, McKinsey, and Goldman Sachs warn of mass job displacement as AI automates millions of roles globally. From Microsoft’s mass layoffs to Indian IT giants slashing support roles, the AI storm is hitting every sector. Bengaluru CEOs are calling out declining coding standards and warning of a digital revolution that may spare none. This special BTTV report breaks down what’s happening, who’s at risk, and how fast this shift is unfolding. Watch now to understand the future of work — before it’s too late.
| 2025-07-04T00:00:00 |
2025/07/04
|
https://www.businesstoday.in/bt-tv/video/ai-layoffs-begin-ceos-admit-white-collar-jobs-will-suffer-in-next-5-years-483116-2025-07-04
|
[
{
"date": "2022/12/01",
"position": 74,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/04/01",
"position": 68,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
"position": 66,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
"position": 66,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/11/01",
"position": 75,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/04/01",
"position": 79,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/06/01",
"position": 80,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 65,
"query": "artificial intelligence layoffs"
}
] |
Microsoft's AI-Driven Layoff Strategy: A New Chapter in Workforce ...
|
Microsoft's AI-Driven Layoff Strategy: A New Chapter in Workforce Management
|
https://opentools.ai
|
[] |
In a bold move shaped by artificial intelligence, Microsoft has unveiled a series of layoffs, signaling a strategic shift in how tech giants ...
|
Looking forward, the implications of Microsoft's strategic layoffs may reverberate across the tech industry, setting a precedent for other companies to follow. By focusing on AI and streamlining their operations, Microsoft aims to position itself at the forefront of innovation. This move could lead to more collaborative features within its product offerings, enhancing productivity and efficiency across various sectors. However, this transition also calls for a robust framework to support affected employees, promoting reskilling and upskilling initiatives to ease their transition into new roles. Further analysis on the topic can be found in the full article available at White County Citizen .
| 2022-12-01T00:00:00 |
https://opentools.ai/news/microsofts-ai-driven-layoff-strategy-a-new-chapter-in-workforce-management
|
[
{
"date": "2022/12/01",
"position": 75,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/03/01",
"position": 63,
"query": "AI layoffs"
},
{
"date": "2023/04/01",
"position": 69,
"query": "AI layoffs"
},
{
"date": "2023/04/01",
"position": 78,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 77,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 73,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 63,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
"position": 75,
"query": "AI layoffs"
},
{
"date": "2023/07/01",
"position": 82,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/09/01",
"position": 78,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 78,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 88,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/01/01",
"position": 71,
"query": "AI layoffs"
},
{
"date": "2024/04/01",
"position": 69,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/05/01",
"position": 77,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 77,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 72,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 79,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 80,
"query": "artificial intelligence layoffs"
}
] |
|
Following mass layoffs, Xbox exec recommends AI to cope - Mashable
|
Following mass layoffs, Xbox exec recommends AI to cope
|
https://mashable.com
|
[] |
Many tech companies have been whittling down their workforces, citing changing markets and the impact of generative AI. A handful of CEOs, ...
|
An Xbox exec suggested Microsoft CoPilot can reduce the cognitive load of the company's own layoffs.
An Xbox exec suggested Microsoft CoPilot can reduce the cognitive load of the company's own layoffs. Credit: Thomas Trutschel / Photothek via Getty Images
The thousands of recently terminated Microsoft employees, navigating one of the company's largest layoffs in years amid a period of industry upheaval, already have a tool to cope with the emotional burden, according to one Xbox exec: Microsoft Copilot.
The sentiment was shared in a now-deleted LinkedIn post by Xbox Game Studios Publishing executive producer Matt Turnbull, captured at the time by gaming blog Aftermath, which reads:
"I know these types of tools engender strong feelings in people, but I'd be remiss in not trying to offer the best advice I can under the circumstances. I've been experimenting with ways to use LLM AI tools (like ChatGPT or CoPilot) to help reduce the emotional and cognitive load that comes with job loss."
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On July 1, Xbox's parent company announced it would be terminating around 9,000 employees — about four percent of its workforce — in a move intended to ensure the company was set up for success in a "dynamic marketplace." The job cuts affected the company's gaming division, mainly Xbox staffers — just a few months prior, Microsoft cut 6,000 jobs, providing the same reasoning as recent layoffs and in the wake of a round of cuts in 2023 that saw 10,000 employees heading out its doors.
Mashable Light Speed Want more out-of-this world tech, space and science stories? Sign up for Mashable's weekly Light Speed newsletter. Loading... Sign Me Up By clicking Sign Me Up, you confirm you are 16+ and agree to our Terms of Use and Privacy Policy Thanks for signing up!
Many tech companies have been whittling down their workforces, citing changing markets and the impact of generative AI. A handful of CEOs, including Meta's Mark Zuckerberg and Klarna CEO Sebastian Siemiatkowski, have outrightly stated their intent to replace jobs and workflows with AI.
"At a time when mental energy is scarce, these tools can help get you unstuck faster, calmer, and with more clarity," Turnbull wrote, recommending using CoPilot for help with both job hunting and gaining "emotional clarity and confidence." Microsoft CoPilot has reportedly become a non-negotiable for Microsoft employees in recent months, as it struggles to sell the assistant to other companies.
In May, CEO Mustafa Suleyman said the company was trying to market CoPilot as an emotionally therapeutic confidant to Gen Z and millennial customers, with the agent now able to "sense a user’s comfort boundaries, diagnose issues, and suggest solutions," reported Fortune.
Broadly, professionals have warned about using AI-powered chatbots as a replacement for human therapy and emotional connection. In January, the American Psychological Association sent a letter urging the Federal Trade Commission to investigate harmful chatbots that deceptively advertise psychological or mental health support. The use of AI tools by therapists themselves, including agentic AI, recording and transcription tools, and notetakers, has prompted further concerns about digital privacy.
But tech is still courting consumers by building more "emotionally intelligent" agents, including Microsoft's CoPilot. OpenAI CEO Sam Altman has called ChatGPT a life adviser for young adults, for example, even as watchdogs have warned about using the tool for therapy.
| 2025-07-06T00:00:00 |
2025/07/06
|
https://mashable.com/article/microsoft-xbox-recommend-ai-for-layoff-aftermath
|
[
{
"date": "2022/12/01",
"position": 77,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/04/01",
"position": 75,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2023/05/01",
"position": 59,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
"position": 40,
"query": "AI layoffs"
},
{
"date": "2023/06/01",
"position": 70,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
"position": 41,
"query": "AI layoffs"
},
{
"date": "2023/07/01",
"position": 74,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/11/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2023/11/01",
"position": 80,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/01/01",
"position": 42,
"query": "AI layoffs"
},
{
"date": "2024/04/01",
"position": 63,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/05/01",
"position": 37,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2024/06/01",
"position": 69,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/07/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 39,
"query": "AI layoffs"
},
{
"date": "2024/08/01",
"position": 69,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/12/01",
"position": 37,
"query": "AI layoffs"
},
{
"date": "2025/07/06",
"position": 58,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/07/06",
"position": 54,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/07/06",
"position": 54,
"query": "artificial intelligence layoffs"
},
{
"date": "2025/07/06",
"position": 29,
"query": "artificial intelligence layoffs"
}
] |
Microsoft cuts 9000 workers in second wave of major layoffs - AFR
|
Microsoft cuts 9000 workers in second wave of major layoffs
|
https://www.afr.com
|
[
"Brody Ford",
"Matt Day",
"Primrose Riordan",
"Sally Patten",
"Mandy Coolen",
"John Davidson",
"Nick Lenaghan"
] |
Across the tech industry, companies are grappling with the spiralling costs of staying up to date in the artificial intelligence race, whether ...
|
Microsoft has begun job cuts that will impact about 9000 workers, its second major wave of layoffs this year as it seeks to control costs while ramping up on artificial intelligence spending.
Less than 4 per cent of the company’s total workforce will be impacted, a spokesperson said. The cuts will have an impact across teams, geographies and tenure and are being made in a bid to streamline processes and reduce layers of management, the spokesperson said.
Loading...
Bloomberg
| 2025-07-03T00:00:00 |
2025/07/03
|
https://www.afr.com/technology/microsoft-cuts-9000-workers-in-second-wave-of-major-layoffs-20250703-p5mc3o
|
[
{
"date": "2022/12/01",
"position": 92,
"query": "artificial intelligence layoffs"
}
] |
Microsoft Cuts 9000 Workers in Second Wave of Major Layoffs
|
Microsoft Cuts 9,000 Workers in Second Wave of Major Layoffs
|
https://www.bloomberg.com
|
[
"Brody Ford",
"Matt Day",
"Brody Ford Matt Day"
] |
The job cuts may help offset rising spending on AI infrastructure and reflect a greater push to use AI tools internally, according to an analyst ...
|
Microsoft Corp. began job cuts that will impact about 9,000 workers, its second major wave of layoffs this year as it seeks to control costs while ramping up on artificial intelligence spending.
Less than 4% of the company’s total workforce will be impacted, a spokesperson said. The cuts will have an impact across teams, geographies and tenure and are made in an effort to streamline processes and reduce layers of management, the spokesperson added.
| 2025-07-02T00:00:00 |
2025/07/02
|
https://www.bloomberg.com/news/articles/2025-07-02/microsoft-to-cut-9-000-workers-in-second-wave-of-major-layoffs
|
[
{
"date": "2022/12/01",
"position": 96,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/04/01",
"position": 74,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/05/01",
"position": 67,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/06/01",
"position": 78,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/07/01",
"position": 75,
"query": "artificial intelligence layoffs"
},
{
"date": "2023/11/01",
"position": 78,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/04/01",
"position": 66,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/06/01",
"position": 67,
"query": "artificial intelligence layoffs"
},
{
"date": "2024/08/01",
"position": 72,
"query": "artificial intelligence layoffs"
}
] |
III. The Current State of Artificial Intelligence in Education | NEA
|
III. The Current State of Artificial Intelligence in Education
|
https://www.nea.org
|
[
"National Education Association"
] |
Artificial intelligence is a rapidly growing technology that is actively changing how we teach, learn, work, and live. AI can be found nearly everywhere.
|
Artificial intelligence is a rapidly growing technology that is actively changing how we teach, learn, work, and live. AI can be found nearly everywhere. Many people knowingly and unknowingly interact with AI daily, from mobile phones and computer applications to surveillance systems and media streaming services. In its most basic definition, artificial intelligence is any machine-based system designed around human-defined objectives to perform tasks that would otherwise require human or animal intelligence. Varying in purpose and programming, AI systems can do one or more of the following: identify patterns, understand natural language, produce content, make predictions and decisions, give recommendations, solve problems, or adapt to and learn from new information and circumstances.
In general, current artificial intelligence tools can be broken down into three categories:
Reactive AI tools respond to specific inputs or situations without learning from past experiences—for example, AI assistants, such as Alexa and Siri, or household tools, such as Roomba vacuums.
tools respond to specific inputs or situations without learning from past experiences—for example, AI assistants, such as Alexa and Siri, or household tools, such as Roomba vacuums. Predictive AI tools analyze historical data and experiences to predict future events or behaviors, such as when Amazon or Netflix shows you suggested items.
tools analyze historical data and experiences to predict future events or behaviors, such as when Amazon or Netflix shows you suggested items. Generative AI tools, such as ChatGPT and Gemini, generate novel text, images, videos, or other content based on existing data patterns and structures.
While generative AI has received the greatest attention in recent months, it is important to realize that humans have been using reactive and predictive AI tools for quite some time. In addition, AI technology is developing very quickly, with new models and tools introduced frequently.
Artificial intelligence employed in pre-K–12 and higher education contexts can take on a variety of forms. A report by Education International provides a helpful construct for these uses:
Student-Focused AI includes adaptive tutoring systems, automatic writing evaluation systems, and chatbots, among other tools aimed at supporting students.
includes adaptive tutoring systems, automatic writing evaluation systems, and chatbots, among other tools aimed at supporting students. Teacher-Focused AI tools are aimed at teachers and include assessment supports, lesson planning tools, and resource curation systems.
tools are aimed at teachers and include assessment supports, lesson planning tools, and resource curation systems. Institution-Focused AI helps with school and campus administration and operations, such as handling scheduling, scanning for safety concerns, and identifying students at risk.
A fourth type, system-focused AI, has also begun to emerge, with some states using AI to determine school funding or score state assessments.
Recognizing this power, capability, and financial opportunity, technology companies and developers are actively finding ways to integrate AI into education systems worldwide. Yet, at this point, many uses of AI in education are largely speculative, without a strong, independent research base showing that these tools are more effective than existing practices or technologies.
Nonetheless, students and educators have started to embrace artificial intelligence, particularly generative AI. A 2024 report by the Center for Democracy & Technology found that the percentage of K–12 teachers who reported using a generative AI tool for personal or school use jumped 32 percentage points, to 83 percent, between the 2022–2023 school year and 2023–2024. In the same study, 59 percent of teachers reported that they are certain at least one of their students has used generative AI for school purposes. In higher education, 49 percent of students reported using generative AI regularly as of September 2023, although only 22 percent of faculty reported this level of usage.
This surge in artificial intelligence, and particularly in generative AI, requires that educators become prepared to assess when it is appropriate to use AI, help their students become AI literate, and advocate for the development of policies about this technology. In other words, educators must be able to not only teach with AI but also teach about AI. Yet, opportunities for educators to get up to speed are still lacking. In a survey taken earlier this year, Education Week found that 71 percent of K–12 teachers had received no professional learning about using artificial intelligence in the classroom.
As of this writing, only 16 states—Arizona, California, Connecticut, Hawaii, Indiana, Kentucky, Michigan, Mississippi, North Carolina, Ohio, Oklahoma, Oregon, Utah, Virginia, Washington, and West Virginia—have issued guidance from the state department of education or another organization. Meanwhile, Tennessee has mandated that districts develop their own policies. New York has issued a statewide ban on the use of facial recognition in education settings. Districts and higher educational institutions have also varied greatly in their approaches, with some banning AI outright (although, some of those bans have since been lifted), some putting policies in place about appropriate use, and some, like the Los Angeles Unified School District, building AI-powered platforms. Education systems are clearly in a transitional phase in terms of determining when and how to harness AI.
Navigating this significant technological shift will require intense attention and involvement by the NEA, its state and local affiliates, and its members. Members and affiliates need to be prepared to be leaders at their schools and campuses and in policy discussions at all levels of the education system. The needs of students and educators must be at the forefront during the development, selection, implementation, and evaluation of AI technologies to ensure that these tools support effective teaching and learning, not the agendas of for-profit entities or those who would like to undermine public education by replacing school staff with computers. We must also be ready to hold AI developers accountable to protect data privacy and intellectual property rights, mitigate algorithmic bias and inaccurate or nonsensical outputs, and diminish environmental hazards.
This report provides background to the proposed Policy Statement on the Use of Artificial Intelligence in Education by providing an overview of the promise of AI in education, reviewing existing NEA policies, and providing background research and information on each of the five principles to support the safe, effective, and equitable use of AI technologies in schools and on campuses. The Task Force acknowledges that AI is developing and changing at a rapid pace, and thus, policies must be adaptable and reviewed regularly. Our goal for the Policy Statement and this accompanying report is to provide a starting point for an ongoing conversation about how artificial intelligence should and will become a part of education and society.
| 2022-12-01T00:00:00 |
https://www.nea.org/resource-library/artificial-intelligence-education/iii-current-state-artificial-intelligence-education
|
[
{
"date": "2022/12/01",
"position": 17,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 16,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 13,
"query": "artificial intelligence education"
},
{
"date": "2024/01/01",
"position": 8,
"query": "AI education"
},
{
"date": "2024/02/01",
"position": 9,
"query": "AI education"
},
{
"date": "2024/03/01",
"position": 9,
"query": "AI education"
},
{
"date": "2024/05/01",
"position": 10,
"query": "AI education"
},
{
"date": "2024/07/01",
"position": 9,
"query": "AI education"
},
{
"date": "2024/09/01",
"position": 14,
"query": "artificial intelligence education"
},
{
"date": "2024/10/01",
"position": 9,
"query": "AI education"
},
{
"date": "2024/12/01",
"position": 14,
"query": "artificial intelligence education"
},
{
"date": "2025/02/01",
"position": 20,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 17,
"query": "artificial intelligence education"
}
] |
|
Artificial intelligence in education - UNESCO
|
Artificial intelligence in education
|
https://en.unesco.org
|
[] |
AI has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the ...
|
Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and accelerate progress towards SDG 4. However, rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the Education 2030 Agenda, while ensuring that its application in educational contexts is guided by the core principles of inclusion and equity.
UNESCO’s mandate calls inherently for a human-centred approach to AI. It aims to shift the conversation to include AI’s role in addressing current inequalities regarding access to knowledge, research and the diversity of cultural expressions and to ensure AI does not widen the technological divides within and between countries. The promise of “AI for all” must be that everyone can take advantage of the technological revolution under way and access its fruits, notably in terms of innovation and knowledge.
Within the framework of the Beijing Consensus, UNESCO developed Artificial intelligence and education: Guidance for policy-makers to foster the readiness of education policy-makers in artificial intelligence. It aims to generate a shared understanding of the opportunities and challenges that AI offers for education, as well as its implications for the core competencies needed in the AI era. UNESCO also published AI competency frameworks for students and teachers to guide countries in supporting students and teachers to understand the potential as well as risks of AI.
| 2022-12-01T00:00:00 |
https://en.unesco.org/artificial-intelligence/education
|
[
{
"date": "2022/12/01",
"position": 22,
"query": "artificial intelligence education"
},
{
"date": "2023/06/01",
"position": 20,
"query": "AI education"
},
{
"date": "2023/09/01",
"position": 19,
"query": "AI education"
},
{
"date": "2023/11/01",
"position": 18,
"query": "AI education"
},
{
"date": "2023/11/01",
"position": 22,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 18,
"query": "artificial intelligence education"
},
{
"date": "2024/01/01",
"position": 20,
"query": "AI education"
},
{
"date": "2024/03/01",
"position": 20,
"query": "AI education"
},
{
"date": "2024/05/01",
"position": 18,
"query": "AI education"
},
{
"date": "2024/07/01",
"position": 20,
"query": "AI education"
},
{
"date": "2024/09/01",
"position": 19,
"query": "artificial intelligence education"
},
{
"date": "2024/10/01",
"position": 19,
"query": "AI education"
},
{
"date": "2024/11/01",
"position": 18,
"query": "AI education"
},
{
"date": "2024/12/01",
"position": 18,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence (AI) in Education
|
Artificial Intelligence (AI) in Education
|
https://www.intel.com
|
[] |
AI in education can help improve learning outcomes, automate tasks, and protect schools from cyber threats to benefit students, teachers, and staff.
|
What Is AI in Education?
AI is a technology that enables machines to perform tasks that typically require human intelligence, learning, adapting, and acting on large amounts of data it rapidly processes. This capability is being applied in education technology to help augment and personalize student learning, automate repetitive or time-consuming tasks, and protect personal data and school technology to benefit students, teachers, and administrators.
Why AI in Education?
The use of education technology (edtech) in schools is becoming more mainstream. Similarly, the use of AI, a technology that enables machines to perform tasks that typically require human intelligence, is becoming more mainstream in everyday life. Bringing the two together is a natural step that can benefit students, teachers, staff, and administrators.
AI can take many shapes and forms in education. Most recently, there’s a growing interest in generative AI (GenAI) tools that can create content and complete tasks in response to naturally phrased questions. These systems can personalize content, deliver near-real-time feedback, and guide coaching and skills development.
Incorporating intelligent AI technology into schools can also help teachers, staff, and administrators by automating repetitive or time-consuming tasks, freeing time to focus on enabling student success.
By the Numbers
Recent research on AI in education conducted by multiple institutions has found:
| 2022-12-01T00:00:00 |
https://www.intel.com/content/www/us/en/learn/ai-in-education.html
|
[
{
"date": "2022/12/01",
"position": 31,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 29,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 32,
"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 35,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 31,
"query": "artificial intelligence education"
},
{
"date": "2025/02/01",
"position": 34,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 29,
"query": "artificial intelligence education"
}
] |
|
AI4K12 – Sparking Curiosity in AI
|
AI4K12
|
https://ai4k12.org
|
[] |
The initiative is developing (1) national guidelines for AI education for K-12, (2) an online, curated resource Directory to facilitate AI instruction.
|
5 Big Ideas in AI
A poster you can print, and an illustrative graphic. Click for copyright and licensing information. Short link: https://bit.ly/ai4k12-five-big-ideas. This poster is also available in multiple languages.
We have icons available for each of the Five Big Ideas on the poster page under Resources.
| 2022-12-01T00:00:00 |
https://ai4k12.org/
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[
{
"date": "2022/12/01",
"position": 40,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 40,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 42,
"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 42,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 44,
"query": "artificial intelligence education"
},
{
"date": "2025/02/01",
"position": 43,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 41,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence (AI) Guidance | U.S. Department of Education
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Artificial Intelligence (AI) Guidance
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https://www.ed.gov
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[] |
Learn more about federal Artificial Intelligence (AI) guidance, innovation, and risk management, and find an inventory of ED's AI use-cases.
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Program Office Office Function Use Case Description
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Aidan Chat-bot Federal Student Aid's (FSA) virtual assistant uses natural language processing to answer common financial aid questions and help customers get information about their federal aid on StudentAid.gov.
In just over two years, Aidan has interacted with over 2.6 million unique customers, resulting in more than 11 million user messages.
Office of Finance and Operations To transform the Department into a high-performance, customer-focused organization by providing services to our customers that help them do a better job of managing their people, processes, and overall strategy. IPAC RPA Bot The IPAC Workflow bot downloads a date range csv file from the Treasury's IPAC system, adjusts the entries to fit the ED system, and uploads them to the ED SharePoint sites and the Financial system.
Office of Finance and Operations To transform the Department into a high-performance, customer-focused organization by providing services to our customers that help them do a better job of managing their people, processes, and overall strategy. CAISY - Artificial Intelligence System Skillsoft Percipio Assessment for the Department Use CAISY is designed to enhance decision-making and operational efficiency by delivering real-time insights, predictive analytics, and intelligent automation. By streamlining repetitive tasks and improving situational awareness, it helps organizations reduce costs, boost productivity, and make more accurate, data-driven decisions.
Office of the Under Secretary Coordinates policies, programs, and activities related to Postsecondary Education (OPE), Career, Technical Education, Adult Education (OCTAE), The Office of the Chief Economist, and Federal Student Aid (FSA). Speech to Text Meeting Transcription Otter.AI is designed to enhance communication and productivity by providing real-time transcription and collaboration tools for meetings, interviews, lectures, and other audio interactions. It enables users to focus on conversations while generating accurate, searchable transcripts, improving accessibility, saving time, and streamlining the organization of critical information.
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Generative AI and Foundation Model Integration AWS Bedrock enables organizations to build and scale applications powered by foundation models, supporting use cases such as text generation, summarization, conversational AI, personalization, and content creation, with seamless integration into existing AWS services and infrastructure.
Contracts & Acquisitions Management Responsible for the solicitation, award, administration, and closeout of all contracts and other acquisition instruments, except for Office of Federal Student Aid procurements and some simplified acquisitions and General Services Administration schedule orders. Generative AI - Text Generation AI generates summaries and outlines for projects and presentations, helping to organize key points and structure content effectively. This assists users in quickly preparing for discussions, reports, or presentations by focusing on the most relevant information.
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Generative AI - Code Generation When given written paragraphs as prompts, AI generates code snippets in a variety of programming languages. This capability allows users to quickly obtain relevant code tailored to their specific requirements.
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Generative AI - Idea Suggestion When prompted with written paragraphs, AI suggests possible courses of actions for given scenarios.
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Generative AI - Information Summarization When provided with publicly available documents or articles, AI extracts key points, generates summaries, and identifies relevant action items. This helps users quickly grasp the essential information and determine next steps without having to read the entire content.
Federal Student Aid Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs. Generative AI - Text Generation When provided with outlines or ideas, AI provides sample paragraphs that may be used to assist in the writing process.
Grants Management Policy Division Coordinates with all components of ED to develop and institute Department-wide grant policies and procedures for formula and discretionary grants management. Generative AI - Code Generation When given written paragraphs as prompts, AI generates code snippets in a variety of programming languages. This capability allows users to quickly obtain relevant code tailored to their specific requirements.
Grants Management Policy Division Coordinates with all components of ED to develop and institute Department-wide grant policies and procedures for formula and discretionary grants management. Generative AI - Design Generation AI will create paragraphs and images to augment visual designs for web design and communication.
Institute of Education Sciences The nation's leading source for rigorous, independent education research, evaluation, and statistics. Generative AI - Code Generation Assisting in the development of code used for data analysis, AI is given descriptions of desired functions and returns snippets of R code.
Institute of Education Sciences The nation's leading source for rigorous, independent education research, evaluation, and statistics. Generative AI - Code Generation When prompted with SAS Syntax, AI will provide suggestions of code optimization opportunities.
Institute of Education Sciences The nation's leading source for rigorous, independent education research, evaluation, and statistics. Generative AI - Text Generation When provided with outlines or ideas, AI provides sample paragraphs that may be used to assist in the writing process.
Office of the Chief Data Officer Managing and improving the Department's ability to leverage data as a strategic asset. Generative AI - Code Generation AI is used to train employees on the benefits of prompt engineering, by providing snippets of code, allowing users to compare the performance of the generated code.
Office of the Chief Data Officer Managing and improving the Department's ability to leverage data as a strategic asset. Generative AI - Data Manipulation
AI is used to automate the conversion between related data types, such as translating a county name into its corresponding county FIPS (Federal Information Processing Standards) code, recognizing and mapping the relationship between different forms of data.
Office of the Chief Data Officer Managing and improving the Department's ability to leverage data as a strategic asset. Generative AI - Idea Suggestion AI is used to assist in training employees by generating paragraphs and images related to the topic of study.
Office of the Chief Data Officer Managing and improving the Department's ability to leverage data as a strategic asset. Generative AI - Mock Data Generation AI is used to generate mock data sets for testing purposes. These simulated datasets help assess system performance, validate models, and ensure the accuracy of data-driven processes without using real-world data.
Office of the Chief Information Officer Implementing the operative principles identified in the Act requiring the establishment of a management framework to improve the planning and control of information technology investments and leading change to improve the efficiency and effectiveness of agency operations. Generative AI - Code Generation AI is utilized to generate sample code snippets that assist users in understanding programming language syntax and addressing troubleshooting challenges by providing clear, contextually relevant examples.
Office of the Chief Information Officer Implementing the operative principles identified in the Act requiring the establishment of a management framework to improve the planning and control of information technology investments and leading change to improve the efficiency and effectiveness of agency operations. Generative AI - Idea Suggestion AI can assist in generating segments of outreach strategies, creating project plans, and brainstorming ideas. It helps streamline the process by providing structured suggestions and innovative concepts for various aspects of planning and communication.
Office of the Chief Information Officer Implementing the operative principles identified in the Act requiring the establishment of a management framework to improve the planning and control of information technology investments and leading change to improve the efficiency and effectiveness of agency operations. Generative AI - Information Summarization AI is used to research and analyze public articles and policies, extracting key action items enabling users to quickly identify important takeaways and actionable insights from complex or lengthy documents.
Office of the Chief Information Officer Implementing the operative principles identified in the Act requiring the establishment of a management framework to improve the planning and control of information technology investments and leading change to improve the efficiency and effectiveness of agency operations. Generative AI - System Testing AI will be provided with fabricated examples of prohibited prompt information, to ensure proper filters and data loss prevention methods are in place.
Office of the Chief Information Officer Implementing the operative principles identified in the Act requiring the establishment of a management framework to improve the planning and control of information technology investments and leading change to improve the efficiency and effectiveness of agency operations. Generative AI - Text Generation AI is leveraged to suggest descriptive paragraphs, workflow diagrams, and qualifications for public positions. This helps streamline the creation of detailed content, visual representations, and role requirements, ensuring clarity and relevance for various public sector needs.
Office of Communications and Outreach Responsible for overall leadership for the Department in its communications and outreach activities. These activities are designed to engage the general public, including a wide variety of education, community, business, parent, student, and other organizations, in the U.S. Department of Education's mission. Generative AI - Text Generation AI is used to assist with message communication in written paragraphs, crafting text designed to capture the attention of viewers. By analyzing tone, structure, and content relevance, AI helps generate compelling and engaging messages that resonate with the target audience.
Office for Civil Rights Responsible for overall leadership for the Department in its communications and outreach activities. These activities are designed to engage the general public, including a wide variety of education, community, business, parent, student, and other organizations, in the U.S. Department of Education's mission. Generative AI - Code Generation AI is used to create sample DAX code to assist in the creation of charts and tables for data analysis.
Office for Civil Rights Responsible for overall leadership for the Department in its communications and outreach activities. These activities are designed to engage the general public, including a wide variety of education, community, business, parent, student, and other organizations, in the U.S. Department of Education's mission. Generative AI - Text Generation AI is provided with publicly available examples of document structures and is prompted to generate new documents based on these formats. This assists in the creation of consistent, well-organized documents that adhere to established structures, making content creation more efficient and standardized.
Office of Career, Technical, and Adult Education Administers, coordinates programs that are related to adult education and literacy, career and technical education, and community colleges. Generative AI - Text Generation AI provides examples of the latest technologies that can aid in the development of code. By analyzing current trends and tools, AI suggests relevant technologies, frameworks, and libraries that can enhance coding efficiency, improve performance, and streamline development processes.
Office of Career, Technical, and Adult Education Administers, coordinates programs that are related to adult education and literacy, career and technical education, and community colleges. Generative AI - Text Generation AI is provided with publicly available images and tasked with describing them to ensure 508 accessibility compliance, generating detailed, clear descriptions of the images to make them accessible to individuals with visual impairments, meeting accessibility standards for digital content.
Office of the Deputy Secretary Assists the Secretary in the discharge of Secretarial duties and responsibilities. Generative AI - Code Generation When prompted with descriptive paragraphs or specific queries, AI generates code snippets designed to assist with data analysis and data visualizations.
Office of the Deputy Secretary Assists the Secretary in the discharge of Secretarial duties and responsibilities. Generative AI - Information Summarization AI is used to research and analyze public reports, policies, and media, extracting key action items enabling users to quickly identify important takeaways.
Office of the Deputy Secretary Assists the Secretary in the discharge of Secretarial duties and responsibilities. Generative AI - Text Generation AI is used to generate email templates and draft content intended for public consumption. By analyzing the intended message and audience, AI creates well-structured, engaging templates and content that can be easily customized for various communication needs.
Office of Elementary and Secondary Education To empower States, districts, and other organizations to meet the diverse needs of every student by providing leadership, technical assistance, and financial support. Generative AI - Code Generation When prompted with descriptive paragraphs or specific queries, AI generates code snippets designed to assist with data analysis and troubleshooting in Power BI and Python. These snippets can include tasks such as creating data visualizations, performing data transformations, writing DAX expressions for Power BI, or debugging Python scripts.
Office of Elementary and Secondary Education To empower States, districts, and other organizations to meet the diverse needs of every student by providing leadership, technical assistance, and financial support. Generative AI - Information Summarization AI is used to search publicly available awards and progress reports to identify trends in specific programs and provide summary narratives.
Office of Elementary and Secondary Education To empower States, districts, and other organizations to meet the diverse needs of every student by providing leadership, technical assistance, and financial support. Generative AI - Text Generation AI provides public policy analysis, templates for written materials, sample paragraphs, and drafts of memos by utilizing publicly available data. This helps streamline the creation of policy documents, reports, and communications, ensuring they are well-informed and aligned with current trends and regulations.
Office of Educational Technology Develops educational technology policy and establishes strategies for encouraging the development and use of educational technology. Generative AI - Text Generation AI is used to generate text for many uses, including: sample drafts for emails, potential blog posts for schools and districts, and social media posts, titles, blogs, and webinars.
Office of Finance and Operations To transform the Department into a high-performance, customer-focused organization by providing services to our customers that help them do a better job of managing their people, processes, and overall strategy. Generative AI - Capability Evaluation Using AI, a study is performed identifying places where AI is commonly used to improve business processes.
Office of Finance and Operations To transform the Department into a high-performance, customer-focused organization by providing services to our customers that help them do a better job of managing their people, processes, and overall strategy. Generative AI - Code Generation When prompted with descriptive paragraphs or specific queries, AI generates code snippets designed to assist with data analysis and data visualizations.
Office of Finance and Operations To transform the Department into a high-performance, customer-focused organization by providing services to our customers that help them do a better job of managing their people, processes, and overall strategy. Generative AI - Text Generation Paragraphs are generated, assisting employees in project plan documentation, writing program communications and offerings, and identifying potential learning materials.
Office of the General Counsel Provides legal assistance to ED, including the provision of legal advice, litigation, and legislative services, and is responsible for performing certain law-related management functions, including managing the Department’s ethics program. Generative AI - Capability Evaluation Approved AI tools undergo thorough testing to evaluate their capability to provide accurate, reliable, and comprehensive legal information, ensuring the tools meet established standards for precision and dependability.
Office of the General Counsel Provides legal assistance to ED, including the provision of legal advice, litigation, and legislative services, and is responsible for performing certain law-related management functions, including managing the Department’s ethics program. Generative AI - Information Summarization To assist in the identification of legal and civil rights issues, AI is used to find and summarize publicly available legal cases involving AI.
Office of the General Counsel Provides legal assistance to ED, including the provision of legal advice, litigation, and legislative services, and is responsible for performing certain law-related management functions, including managing the Department’s ethics program. Generative AI - Information Summarization AI is requested to provide summaries of the strengths and weaknesses of AI tools, and identify opportunities, risks, and benefits of AI usage.
Office of the General Counsel Provides legal assistance to ED, including the provision of legal advice, litigation, and legislative services, and is responsible for performing certain law-related management functions, including managing the Department’s ethics program. Generative AI - Text Generation AI is used to gather information and analyze the structure of contracts, state and federal laws, and collective bargaining agreements. By processing and interpreting relevant legal texts, AI helps users understand complex legal frameworks and identify key elements within these documents.
Office of Postsecondary Education Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students. Generative AI - Capability Evaluation AI systems are tested to assess the range and depth of information they can provide on Congressional Directives, ensuring the AI comprehensively covers relevant topics and delivers accurate and contextually appropriate insights.
Office of Postsecondary Education Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students. Generative AI - Code Generation Using AI, the creation of Excel Macros and Complex Formulae is automated, expediting data analysis.
Office of Postsecondary Education Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students. Generative AI - Idea Suggestion AI is used to research and synthesize topics for deeper discussion, brainstorm project ideas, and explore potential use cases for educators or department stakeholders.
Office of Postsecondary Education Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students. Generative AI - Information Summarization Public AI tools are used to summarize publicly available documents form various federal sources, and identify sections of text relevant to specified topics.
Office of Postsecondary Education Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students. Generative AI - Text Generation AI tools generate drafts of narratives, marketing materials, public webinar scripts, and engagement-related documents. These drafts streamline the creation of content by providing well-structured, relevant text that can be customized for specific communication and outreach needs.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Capability Evaluation AI is tested to provide a review of the potential uses of AI and to provide a comprehensive understanding of the tools.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Image Generation AI is prompted to create images, icons, and other visual aids representing the Team Values created by OSERS teams.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Information Summarization AI tools provide summaries of accomplishment reports in scholarly and leadership fields.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Information Summarization AI tools provide summaries of publicly available assistance materials, publicly shared materials, and public development programs.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Code Generation AI is prompted to generate snippets of VBA code and complex formulas for Excel. These code snippets and formulas assist users in automating tasks, performing advanced data analysis, and enhancing spreadsheet functionality, saving time and improving efficiency.
Office of Special Education and Rehabilitee Services To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation. Generative AI - Code Generation AI tools provide examples of paragraphs to augment written materials including blog posts, presentation for conferences, newsletters, and talking points.
Office of the Under Secretary Coordinates policies, programs, and activities related to Postsecondary Education (OPE), Career, Technical Education, Adult Education (OCTAE), The Office of the Chief Economist, and Federal Student Aid (FSA). Generative AI - Capability Evaluation AI is prompted to provide instances where AI technology can be used to assist in the higher education arena, helping to identify how AI policy should be shaped.
| 2022-12-01T00:00:00 |
https://www.ed.gov/about/ed-overview/artificial-intelligence-ai-guidance
|
[
{
"date": "2022/12/01",
"position": 43,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 44,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 43,
"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 44,
"query": "artificial intelligence education"
},
{
"date": "2024/12/25",
"position": 32,
"query": "AI education"
},
{
"date": "2024/12/01",
"position": 41,
"query": "artificial intelligence education"
}
] |
|
Generative Artificial Intelligence for Education and Pedagogy
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CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy
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https://teaching.cornell.edu
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[] |
The Cornell administration assembled a committee to develop guidelines and recommendations for the use of GAI for education at Cornell.
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In Spring 2023, the Cornell administration assembled a committee to develop guidelines and recommendations for the use of Generative AI for education at Cornell. Their final report evaluating the feasibility, benefits, and limitations of using generative AI technologies in an educational setting and its impact on learning outcomes is below.
To download "Generative Artificial Intelligence for Education and Pedagogy" as a PDF, see: Full CU Committee Report.
Chairs: Kavita Bala, Alex Colvin
Committee members: Morten H. Christiansen, Allison Weiner Heinemann, Sarah Kreps, Lionel Levine, Christina Liang, David Mimno, Sasha Rush, Deirdre Snyder, Wendy E. Tarlow, Felix Thoemmes, Rob Vanderlan, Andrea Stevenson Won, Alan Zehnder, Malte Ziewitz
For the Center for Teaching Innovation's resources, visit Generative Artificial Intelligence.
Executive summary
Educators must take generative artificial intelligence (GAI) into account when considering the learning objectives for their classes, since these technologies will not only be present in the future workplace, but are already being used by students. While these tools have the opportunity to customize the learning experience for individual students and could potentially increase accessibility, they also hold risks. The most obvious risk is that GAI tools can be used to circumvent learning, but they may also hide biases, inaccuracies, and ethical problems, including violations of privacy and intellectual property. To address the risks of GAI while maximizing its benefit, we propose a flexible framework in which instructors can choose to prohibit, to allow with attribution, or to encourage GAI use. We discuss this framework, taking into consideration academic integrity, accessibility, and privacy concerns; provide examples of how this framework might be broadly relevant to different learning domains; and make recommendations for both faculty and administration.
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Section 1: Introduction
Generative artificial intelligence (GAI) has attracted significant attention with the introduction of technologies like ChatGPT, Bard, and Dall-E, among others. This new technology has spurred major investments by Amazon, Google, Microsoft, and spawned many new startups. While there is much excitement about GAI’s potential to disrupt various industries, many have voiced significant concerns about its potential for harmful use [ 1 ]. This excitement and concern has been echoed in the context of education.
In Spring 2023, the Cornell administration assembled a committee to develop guidelines and recommendations for the use of GAI for education at Cornell, with the following charges:
Evaluate the feasibility and benefits and limitations of using AI technologies in an educational setting and its impact on learning outcomes. Assess the ethical implications of use of AI technologies in the classroom. Identify best practices for integrating AI technologies into curriculum and teaching methodology.
Recommend guidelines for the safe and effective use of AI technologies in an educational setting. Provide recommendations for ongoing evaluation and improvement of the use of AI technologies in an educational setting.
The committee included a broad spectrum of educators who span disciplines across the university. Over a series of meetings in Spring 2023, the committee developed the guidelines shared in this report.
Opportunity: GAI has been touted as a potential paradigm shift in education. Proposed benefits include providing a customized learning experience for all learners matching their individual needs; increasing accessibility for students with learning disabilities, anxiety, or language barriers; allowing instructors to scale constructive critiques for iterative learning and improvement in writing; and assisting in tasks in a number of domains including coding, creative composition, and more.
Concerns: Currently, GAI output can include inaccurate information, toxic output, biases embedded in the model through the training process, and infringement of copyrights on material and images. Students can use GAI to circumvent the process of learning and assessment in classes. In cases when GAI tools can serve learning outcomes, lack of affordable access for all students could exacerbate systemic inequalities. In addition, overreliance on these tools risks atrophying students’ ability and willingness to interact with instructors and peers.
Recommendations
Rethink learning outcomes. GAI requires rethinking our goals in teaching and the learning outcomes we hope to achieve. With the tremendous projected impact of GAI in many industries, students will be working in a GAI-enabled world. We strongly encourage instructors to integrate GAI into learning outcomes to focus student education on higher-level learning objectives, critical thinking, and the skills and knowledge that they will need in the future. Address safety and ethics. We expect that GAI technology will continue to improve, and Cornell will play an important role in enabling its ethical and safe use. However, instructors must educate their students about the pitfalls of current technology. They must teach them to approach GAI critically and to validate GAI-produced information rigorously. Explicitly state policies for use of GAI. Instructors should clearly and consistently communicate to students their expectations on the use of GAI in their assignments and classes, including when it is and is not allowed, and what uses of GAI are considered violations of academic integrity. Further, when GAI is permitted, it should be correctly attributed, and instructors should discuss the importance of student validation of the information generated. Many foundational skills will still need to be developed without the use of GAI. In such cases, instructors must directly explain to students why the process of achieving the specified learning outcomes for a class, without reliance on tools that create “shortcuts,” is integral to a student’s academic and personal growth.
We recommend instructors consider three kinds of policies either for individual assignments or generally in their courses.
To prohibit the use of GAI where it interferes with the student developing foundational understanding, skills, and knowledge needed for future courses and careers. To allow with attribution where GAI could be a useful resource, but the instructor needs to be aware of its use by the student and the student must learn to take responsibility for accuracy and correct attribution of GAI-generated content. To encourage and actively integrate GAI into the learning process where students can leverage GAI to focus on higher-level learning objectives, explore creative ideas, or otherwise enhance learning.
Roadmap. We structure this report as follows. In Section 2, we present a brief, high-level overview of
Generative AI technologies in various domains, with links to detailed resources in Appendix A. In Section 3, we present our guidelines for the three policies described above, including discussions on academic integrity and accessibility issues, and examples and recommendations of use cases for various styles of teaching in diverse disciplines (further expanded in Appendices B-G). In Section 4, we make specific recommendations for faculty and administration, and we summarize our discussion in Section 5.
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Section 2: Generative AI capabilities and limitations
The last year has seen qualitative leaps in the capability of Generative AI that took even experienced AI researchers by surprise. These tools share certain commonalities in their use and training, but also have critical differences in capabilities, use cases, and availability. Generative AI models of natural language, such as ChatGPT or Bard, are known as Large Language Models (LLMs) and are trained on massive corpora of textual data. A different method, diffusion, is used to generate images with models trained on massive collections of image data, including artwork. The capabilities of these systems will likely change drastically in the coming years. While we provide resources in Appendix A to survey existing models, we do not intend these to be comprehensive, but rather to provide a snapshot of capabilities and common models to ground the report in specific approaches. Appendix A gives more details on how these models are trained for various domains like text, code, images, and other modalities, and how the technology is currently made available to users.
The current generation of GAI is based on large-scale, deep neural networks. While this technology is not new, recent developments in model design, hardware, and the ability to aggregate large amounts of training data have made it possible to create models that are larger by orders of magnitude. The models are pretrained, which means they are fit to a large number of human-generated examples. The more data that is available, the better generative outputs become in the precision, detail, and memory of their training data, and also in their ability to generalize based on abstract patterns. Sourcing data to train better models has led to many of the ethical concerns about GAI, including (but not limited to): violations of copyright; bias, or even toxicity, deriving from the training data; and forms of plagiarism within the model outputs. However, it is critical to note that these models are not just memorizing and regurgitating these data, and outputs are rarely identical to any single observed training instance.
Current GAI provides capabilities that would not have been thought possible even at the beginning of this decade. The most popular use patterns allow users to "ask" for output using natural language, and then refine those requests based on the system's output. Systems can produce fluent, grammatical, detailed, and mostly accurate descriptions of historical events, in the style of a Wikipedia page, without including any sentence that actually occurs in an original page. When prompted with a description of a computational process and user interface, systems can generate executable program code that could have come from a Stack Overflow post or a GitHub repository, but does not actually occur in either of those services. Diffusion models can produce images of "a hedgehog driving a train in the style of Botticelli" that have never actually existed. The compelling, immediately satisfying nature of these tools led to the explosion of GAI from a niche research area to popular culture, almost overnight.
Understanding the limitations of GAI technologies.
New users often trust GAI models more than they should. While chatbots are increasingly being trained to avoid low-confidence statements, LLMs answer questions and justify their answers using plausible and confident-sounding language, regardless of the quality of the available evidence. LLMs have only one function: given a history of a conversation, they can predict the next word in the conversation. Thus, if asked to justify a sensible response, an LLM will often provide a reasonable-sounding answer based on its training data. However, when asked to justify a nonsensical response, it will use the same techniques, resulting in a reasonable-sounding but false answer.
Users also often overestimate GAI ability by assuming they can do certain computational tasks well that current computing systems are good at; for example, mathematical calculations or finding references. But at time of writing, GAI models are not able to mathematically reason about floating point numbers, although they provide incorrect—but confident-sounding— answers to complex trigonometric questions. GAI models also fluently create plausible but false academic and other references. However, developers are actively working to augment GAI with more traditional tools (calculators, search engines) to address these issues, so GAI models may become more reliable over time.
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Section 3: Guidelines and recommendations for educational settings
Educational settings vary significantly across Cornell: from large lecture halls to seminars, from lab, field, or studio courses to clinical and practicum-based settings. Generative AI has potential uses in almost all of these settings. For example, educators can use GAI to develop lecture outlines and materials, generate multiple versions of assignments, or compose practice problems. Students can use GAI to research topics and areas, iterate on text to improve written work, program, design and create code, art, and music, and many other uses. Depending on the use, however, GAI could end up “doing the learning” that an assignment is designed to elicit, or distorting the value of assessments, depending on student use of GAI tools.
To address these risks and take advantage of these opportunities, instructors should reassess learning outcomes for each class in light of the advent of GAI. To do so, they should consider the expectations of future courses that may build on the understanding and knowledge their course is expected to develop in the student; the expectations that future workplaces will have for workers’ responsible and ethical use of GAI tools; and the new opportunities for the purposeful use of GAI tools to aid learning.
The introduction of calculators into mathematics education provides a useful, if imperfect, analogy. Students in elementary school still learn how to do long division and multiplication. However, once students have mastered these skills, calculators are used in higher level classes to allow students to solve complex problems without being slowed down by the minutiae of arithmetic, and students are taught to use computational tools to assist in solving hard problems. Education curricula in mathematics have adapted to the increasing availability of calculators to prioritize students’ learning basic skills without calculators at first, and later, allow and/or encourage their use to support higher level learning.
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Section 3.1: Academic integrity and accessibility
One major concern around the use of AI is the potential for academic integrity violations where students use these technologies to do assignments when the work itself is meant to develop skills; for example, practicing problem sets, or to assess basic skills before students proceed to higher levels of learning. In these cases, the use of GAI tools may appropriately be prohibited. A second area of concern is if students use these tools without properly citing them, and/or without questioning the underlying mechanisms or assumptions that produce the content. In this case, the need for students to learn to appropriately attribute and critique these tools is key. However, there are also cases in which the use of GAI should be encouraged; for example, to promote the universal accessibility of assignments, or to provide tools that will enhance high-level learning and allow students to be more creative and more productive. Below, we discuss each of these options in turn, bearing in mind that clear policies and processes are critical for a constructive learning environment and trust between the instructor and student.
Section 3.1.1: Prohibiting GAI tools
Current free access to at least limited versions of GAI tools greatly accelerates the concern that students may be tempted to violate academic integrity principles by turning in AI-generated content as their own. ChatGPT’s ability to almost instantly complete assignments for students makes it significantly more tempting for students facing competing priorities or a last-minute deadline crisis.
Given the widespread use of LLMs, a natural demand has arisen for tools to detect the content of LLM output. Many different tools have been developed for this task including TurnItIn, GPTZero, and OpenAI's classifier. However, in the absence of other evidence, technical methods are not currently very helpful for regulating AI usage in the classroom. The objective of LLMs is to produce text with the same statistical properties of natural language, making the detection problem adversarial as GAI improves. LLMs rarely output long snippets of text that are verbatim copies of existing content, which is the basis of traditional plagiarism detection. Therefore, attempts to identify text generated by GAI can only be done statistically. This method will likely continue to produce both false positives and false negatives, and cannot decisively provide evidence of academic integrity violations. Using these methods currently could lead to unfairly identifying academic integrity violations (for example, bias against non-native speakers), creating a lack of trust between the instructor and students, and damaging the learning environment.
Another potential remedy would be for GAI providers to explicitly limit the outputs of their systems to prevent them from answering common basic homework questions, analogous to the way question forums handle homework questions [ 2 ]. While it is technically possible for providers to implement these restrictions, or even for universities to run their own restricted services, the barriers have not proven to be foolproof. There are now many documented examples of GAI "jailbreaks" that allow adversarial parties to work around the restrictions put on GAI models. Jailbreaking works by constructing a long and complex prompt that can be fed into a model to cause it to ignore its constraints and generate in an alternative manner. Jailbreaking has been used to circumvent prohibitions on certain topics or behaviors such as advocating violence; however, it could also easily be used to obtain answers to homework assignments.
Beyond fairness to other students and to faculty, an overarching concern is that if students rely on GAI, they will not put in the practice needed to learn nor gain confidence in their ability to master needed knowledge or skills. Instructors should communicate to students why completing assignments without “shortcuts” is necessary to meet learning outcomes; why meeting specific learning outcomes is necessary to a student’s academic and personal growth; and why academic integrity violations are so harmful to both the individual student and to the larger learning communities at Cornell and beyond.
Faculty may take other measures to avoid the risk of academic integrity violations by moving to assessments and assignments less suited to GAI models; for example, aligning assessments more closely to class content; or moving assessments from take-home to in-class, e.g., timed oral and written exams, or in-class written essays. These forms of assessment could potentially disproportionately impact students with disabilities, although accommodations such as extended time and distraction-free testing zones might help to address these issues. We examine below the dynamic potential for GAI to support students with diverse disabilities; however, we also note that overreliance on these tools can put these students at an even greater disadvantage. Students with disabilities may prefer using these tools to interactions with faculty and other support systems, and become dependent on GAI to meet their needs–especially in the absence of fuller classroom access. They may also face greater vulnerability in proving that they did not violate academic integrity standards.
Section 3.1.2: Attribution, authorship, access, and accountability in the use of GAI
GAI is a very rapidly evolving technology that is in a major state of flux. While companies are swiftly working to identify and fix issues as they are discovered, it is important for instructors to be aware of the risks involved in using GAI as is currently available. Instructors must educate their students about these risks, and develop plans to mitigate the negative impact of risks in their classroom if they decide to use or allow the use of GAI in their teaching.
GAI tools pose potential privacy risks because data that is shared may be used as training data by the third-party vendor providing the service. Therefore, any information that educators are obligated to keep private, for example, under the Family Educational Rights and Privacy Act (FERPA) or the Health Insurance Portability and Accountability Act (HIPAA), should not be shared with such tools or uploaded to these third party vendors of GAI.
GAI tools also have implications for intellectual property rights. Original research or content that is owned by Cornell University, our students, or employees should not be uploaded to these tools, since they can become part of the training data used by the GAI tools. These include student assignments, data produced in projects or research groups, data that contains personally identifiable information, data from research partners (for example, companies) that may contain proprietary information, data that could be protected by copyright, etc.
If a class expects the use of GAI, it is important to make sure all students have equal access to the technology without cost barriers resulting in differential access. Licensing agreements for the use of GAI tools should be provided or negotiated by the institution, while ensuring that the tools do not limit the university’s educational activities and academic freedom, respect privacy and intellectual property rights, and do not impose cost barriers or constraints.
At the time of writing, the US Department of Education has issued a new report [ 3 ] encouraging the development of policies that advance learning outcomes while protecting human decision making and judgment; that focus on data quality of AI models to ensure fairness and unbiased decisions in educational applications; that understand the impact on equity and increase the focus on advancing equity for students. We agree with these important considerations and expect Cornell researchers and educators will contribute to these improvements in GAI.
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Section 3.2: Encouraging the responsible use of GAI tools
GAI will inevitably be part of the future workplace, and thus a tool that all students will eventually need to learn to use appropriately. Consequently, instructors now have the duty to instruct and guide students on ethical and productive uses of GAI tools that will become increasingly common in their post-Cornell careers. GAI also has the potential to provide support for students with disabilities, particularly for individuals who experience difficulties with cognitive processing and focusing, “social scripting” (i.e., neurotypical means of communication), and anxiety. We have discussed above some of the dangers of reliance on GAI in ways that are counterproductive to learning. However, it is also important for faculty to recognize the barriers that students with disabilities face, and how GAI tools can help implement and sustain fuller modes of access and inclusion for all students in the classroom.
Below, we identify settings across a range of different areas of study where use of GAI could advance teaching goals and learning objectives, and make recommendations based on the different needs of each category. Appendices B-G provide detailed examples. This list is not exhaustive, but can help identify immediate practical use cases to instructors in a wide range of disciplines. While we see a range of possible ways in which GAI can be useful in teaching, common themes include:
Use of GAI for individualized practice, help, and tutoring
Use of GAI to generate material for students to analyze, organize, and edit
Use of GAI for routine, preparatory work leading to higher order thinking and analysis
Analysis of GAI’s use and impact in a domain
Practice of the use of GAI as a tool
We now describe the uses within various disciplines and areas of study.
Section 3.2.1: Courses which develop writing as a skill (e.g., the writing seminars)
GAI tools offer opportunities to help students develop their writing skills through assisting in planning, outlining, editing, as well as by providing individualized feedback. However, use of GAI for generating text and editing in the writing process raises major concerns about attribution of work, academic integrity, plagiarism, and failure to develop foundational and advanced writing skills and judgment. Creativity and originality in writing are key learning outcomes that could be threatened by dependency on GAI in the writing process. Guided use of GAI is encouraged as the best approach to further, rather than undermine, learning outcomes. Examples could include the use of GAI to: generate an outline for a written report that students practice revising; summarize themes from a meeting transcript that students organize and prioritize; brainstorm ideas that students then evaluate; or generate lists of sources that students validate and assess. Appendix B outlines detailed examples and scenarios.
Section 3.2.2: Creative courses for music, literature, and art
Creative fields such as art and music have long engaged in discussions on what is “original work” and how technology can enhance creativity. Practitioners, including students, are highly motivated to develop their skills, but may also be eager to use new technologies to create. While there are many opportunities, concerns exist around ethical attribution of sources and copyright violations. This is an evolving field with companies attempting to add attribution into their processes. Current cases already exist of using GAI for creative brainstorming, or development of final artifacts, as a partner to enable higher level creation by the artist, and many other uses. The rest of the academy might look to creative fields for help working through their own disciplinary considerations of how GAI tools might change notions of original work. Appendix C discusses these considerations in more detail.
Section 3.2.3: Courses in the social sciences
In the social sciences, the advent of GAI raises particular concerns for the written assignments, homework, papers, and exams that are a core component of student work in many courses. Passive reliance on GAI by students to generate literature reviews or written work risks undermining the learning objectives of assignments, producing poor quality work, and violating academic integrity standards. Instructors are also encouraged to explore ways to purposefully incorporate GAI into social science courses in ways that enhance student learning. This can include having students evaluate GAI output and explore ways to test its validity. Appendix D and Appendix B discuss use cases.
Section 3.2.4: Mathematics, physical sciences and engineering
Technical and mathematical courses have adjusted well in the past to incorporate new technologies, such as computing and visualization tools. GAI may provide similar opportunities to enhance education in the space of mathematics, physical sciences, and engineering. For example, students and instructors may use LLMs in an explanatory capacity or use LLMs to synthesize code to support data analysis and visualizations. Some of the biggest concerns with current systems is their inaccuracy (“hallucinations”) and circular reasoning. Instructors should make themselves aware of the capabilities of current systems and the fast changing behavior of these systems on mathematical and engineering problems. We recommend educating students on the capabilities and limitations of these systems, prohibiting their use where basic skills need to be developed, and encouraging their use in cases where LLMs can improve student learning. Appendix E gives detailed examples of use cases for courses in this domain.
Section 3.2.5: Courses in programming
GAI is already used heavily in industry to assist in coding through applications like GitHub Copilot. Opportunities for LLMs in programming education exist in: the generation of code from specifications (text to code), the generation of ancillary tools such as tests (code to code), and the generation of explanations or suggestions (code to text). However, the concern is that students will rely on GAI and will not learn the skills necessary to generate working, understandable, and updatable code. They may be unable to move beyond the solutions favored by an AI system, to identify and fix problems, or at worst, to even recognize that alternatives exist. We recommend using GAI in advanced courses as a tutor or helper in programming, but not as the sole creator of code. See Appendix F for details.
Section 3.2.6: Courses in law
For law, GAI threatens the integrity of the take-home exams that are a common feature of many courses. For foundational courses, particularly in the first year core, use of in-person written exams with restricted access to the internet and ability to access GAI is recommended to ensure the validity and integrity of exams. At the same time, GAI tools are in increasingly widespread use in the practice of law and it is important that this shift be addressed in legal education. This could be done through addressing use of GAI in legal practice explicitly in second and third year classes, including examination of these tools in legal research and writing courses. The strong ethical core of the discipline and practice of law should be reflected in how GAI is addressed. Appendix G elucidates further.
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Section 3.3: Use of GAI by instructors for course content creation
Instructors can use GAI to create content; for example, as a first draft for course structure/syllabi, lecture structure, examples, figures and diagrams, etc. Instructors can also generate large banks of practice problems or assessment questions, though it is important to validate any questions assigned to students for accuracy and appropriateness.
We recommend that instructors also follow the guidelines of attribution if they choose to use GAI to produce course materials. This way, faculty can model for students how to use GAI with attribution. This will also provide clarity for students about where GAI is not being used and avoid assumptions by students that they are being provided educational material that the instructor has not personally created or vetted.
While GAI may have selective utility in assisting in providing feedback for low-stakes formative assessment (for example in practice problems), we currently do NOT recommend it be used in summative evaluation of student work. Evaluation and grading of students is among the most important tasks entrusted to faculty, and the integrity of the grading process is reliant on the primary role of the faculty member.
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Section 4: Recommendations for faculty and administration
Based on the aforementioned opportunities and concerns, we believe that the use of GAI technologies can be integrated into teaching in ways that enhance learning objectives, but that these implementations must be accompanied by strategies to improve students’ understanding, and practice of academic integrity. Such strategies may include: 1) instructing students on the necessity of academic integrity, and what it constitutes; 2) guiding students toward scholarly and applied practices consistent with academic integrity; and 3) clarifying faculty’s intentions around learning outcomes. Students should be taught why using GAI in prohibited ways is not just unethical, but also counterproductive to learning essential content and skills. In addition, faculty must instruct students in best practices for using GAI.
We make the following recommendations for faculty:
Faculty should be explicit in identifying expectations regarding the use of GAI tools in each course, and potentially for individual assignments. Cornell resources such as the Center for Teaching Innovation may be helpful in identifying standardized language and clear examples. Faculty are encouraged to identify well-defined learning outcomes to provide rationales for how and when GAI can/cannot be used in a particular course. When GAI is permitted, faculty should be clear about student expectations in terms of documentation and attribution, what work is expected to be produced by the student themselves, and how the student is expected to validate or verify output from GAI. Faculty members are encouraged to engage in ongoing conversations about the importance of academic integrity, including the fact that basic academic integrity principles remain important and still apply regardless of the existence of GAI tools. (See "Communicating Why Academic Integrity Matters"). Integrating critique of current practices and uses of GAI, including ethical issues, into all stages of learning is vital. We currently discourage the use of automatic detection algorithms for academic integrity violations using GAI, given their unreliability and current inability to provide definitive evidence of violations. While faculty may use GAI as a tool for developing teaching materials, we encourage them to adhere to the same standards of attribution that they require of their students. We do not recommend the use of GAI for student assessment.
The Center for Teaching Innovation is available to consult with departments and individual faculty on how to best implement these recommendations.
We make the following recommendations for university administrators:
The Code of Academic Integrity should be updated with clear and explicit language on the use of GAI, specifically indicating that individual faculty have authority to determine when its use is prohibited, attributed, or encouraged, and that use of GAI on assignments by students is only allowed when expressly permitted by the faculty member. When considering a move (back) to more in-person assignments and assessment, policies and practices should consider how doing so could have a disproportionate impact on students with disabilities and other marginalized students. The university should recognize the additional burden on instructors to adapt to the rapidly changing effects of GAI on education, and provide additional support to teaching faculty and staff. The university administration, in consultation with faculty and academic staff, should develop and issue best practices on assessments, in light of the growing tension between the need to ensure academic integrity and the need to ensure access and inclusion for marginalized students.
Specifically, aligning pedagogical practices with Universal Design for Learning (UDL) can promote fuller access and inclusion for all students. While this does necessitate rethinking the current design of classroom and assessment practices, doing so can achieve the dual goals of greater access for students and appropriate integration of AI tools into the classroom.
Finally, GAI technology continues to increase in capability and ubiquity. Tech companies are actively working to incorporate GAI in every aspect of their products, making it increasingly difficult to avoid or even identify its use. The recommendations in this report should provide a framework for immediate and future actions, but they are not the last word. There must be procedures in place to monitor new advances, communicate new capabilities widely, and adapt policies and course technologies.
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Section 5: Conclusions
The impact of Generative AI tools on education is likely to grow over time. The use of these tools already threatens some standard educational approaches and poses challenges for academic integrity. At the same time, GAI is likely to become an important tool across many domains and students must learn about its strengths and limitations. If used thoughtfully and purposefully, GAI has the potential to enhance educational outcomes. For these reasons, we recommend that Cornell adopt a forward-looking approach that incorporates the use or non-use of GAI specifically into learning objectives.
Our core recommendations to faculty are that they reconsider their learning objectives in light of GAI tools, and incorporate explicit directions regarding use of GAI into their syllabi and assignments. We recommend that faculty formally adopt one of the three different approaches, depending on the learning objectives of the course or assignment.
Prohibit use of GAI where its use would substitute for or interfere with core learning objectives, particularly in courses where students are developing foundational knowledge or skills.
use of GAI where its use would substitute for or interfere with core learning objectives, particularly in courses where students are developing foundational knowledge or skills. Allow with attribution the use of GAI where it can serve as a useful resource to support higher level thinking or skill development.
the use of GAI where it can serve as a useful resource to support higher level thinking or skill development. Encourage use of GAI in courses or assignments where it can be used as a tool to allow exploration and creative thinking, or level the playing field for students with disparate abilities and needs.
Our core recommendation to the administration is to provide material support to faculty as they grapple with adapting individual courses to the new reality of GAI tools. For example, the administration should provide assistance in implementing accommodations for new assignment and assessment mechanisms, provide additional TA support when needed for course redesigns, and support faculty as they implement new teaching techniques that may be unfamiliar, and initially perhaps unwelcome, to students.
To guide students to obtain the potential benefits from GAI in enhancing higher order thinking and learning, and to avoid the dangers of GAI undermining the gain of key skills and knowledge, Cornell must take a proactive approach to the use of GAI in education. Our students need to understand both the value and limitations of GAI, not only because they will encounter it on a regular basis in their future careers and lives, but also because many of them are likely to guide its development and use in the future.
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| 2022-12-01T00:00:00 |
https://teaching.cornell.edu/generative-artificial-intelligence/cu-committee-report-generative-artificial-intelligence-education
|
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AI for education | Microsoft Learn
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AI for education
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https://learn.microsoft.com
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[] |
Explore resources and training on how to use artificial intelligence (AI) for educational purposes with Microsoft.
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Microsoft works with communities, nonprofits, education institutions, businesses, and governments to help people excluded from the digital economy gain the skills, knowledge, and opportunity to gain jobs and livelihoods. Learn about AI and access resources and training on in-demand AI and machine learning skills for jobs and organizations.
| 2022-12-01T00:00:00 |
https://learn.microsoft.com/en-us/training/educator-center/topics/ai-for-education
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The Impact of Artificial Intelligence (AI) on Students' Academic ...
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The Impact of Artificial Intelligence (AI) on Students’ Academic Development
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https://www.mdpi.com
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[
"Vieriu",
"Aniella Mihaela",
"Petrea",
"Aniella Mihaela Vieriu",
"Gabriel Petrea"
] |
The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students' ...
|
This study examines the impact of artificial intelligence (AI) technologies on the learning processes and academic performance of students at the National University of Science and Technology POLITEHNICA Bucharest. Specifically, our research aims to explore the types of AI technologies utilized, the frequency of their use, and students’ perceptions regarding their effectiveness in improving academic performance. Additionally, this study investigates the concerns and challenges associated with the integration of AI in education.
A key challenge is ensuring AI complements, rather than replaces, human interaction. While AI automates tasks and provides data-driven insights, it lacks the empathy, creativity, and nuanced understanding of human educators ( Holmes & Tuomi, 2022 ). Over-reliance on AI for assessment and feedback may reduce opportunities for meaningful dialogue and reflection, essential for higher-order thinking ( Facione, 2020 ). Ethical concerns, such as data privacy, algorithmic bias, and the digital divide, must also be addressed to ensure equitable and inclusive learning environments ( O’Neil, 2016 ).
Despite advancements, AI integration in education raises critical questions about its alignment with established learning theories. While studies have explored challenges ( Hwang et al., 2020 ), obstacles ( T. Baker et al., 2019 ), and future perspectives ( Pinkwart, 2016 ), few explicitly analyze AI’ s diverse functions and their relationship with pedagogical frameworks. For instance, how do AI tools align with constructivist or socio-cultural theories emphasizing collaboration, context, and critical thinking? ( Luckin et al., 2016 ) Additionally, the long-term impact of AI on teaching, learning, and educational equity remains underexplored ( Selwyn, 2022 ).
As data processing and computing technologies have evolved, artificial intelligence (AI) has been increasingly applied in the educational field, often referred to as Artificial Intelligence in Education (AIED). Applications such as intelligent tutoring systems, educational robots, learning analytics dashboards, adaptive learning platforms, and human–computer interactions have demonstrated significant potential for enhancing teaching and learning ( Chen et al., 2020 Zawacki-Richter et al., 2019 ). For example, intelligent tutoring systems have been shown to provide personalized feedback and support, improving student engagement and learning outcomes ( Luckin et al., 2016 ). Similarly, adaptive learning platforms leverage AI to tailor educational content to individual learners’ needs, promoting more effective and efficient learning experiences ( Holmes et al., 2019 ).
The use of AI in education is not without its challenges. The effective integration of artificial intelligence in education requires a thorough understanding of both the technology and the learning process. This complexity is further heightened by ethical concerns, especially in the context of the increasing use of generative artificial intelligence. For instance, Qadir 2023 ) highlights the risk of students misusing AI tools in dishonest or unauthorized ways, such as using AI-generated content to complete assignments without proper attribution. Additionally, concerns about the application of AI in surveillance, control, and assessment practices could undermine trust and autonomy in educational settings ( Williamson, 2017 ). Higher education institutions must clearly define the role and extent of AI in student education to address these challenges effectively ( Holmes & Tuomi, 2022 ).
In traditional education, students are encouraged to take an active role in their learning process by developing skills in exploration, analysis, and problem solving. Critical thinking skills are essential for shaping students’ overall learning experiences. Educators often rely on questioning techniques, collaborative activities, and assignments to enhance students’ ability to evaluate information and develop independent perspectives ( Facione, 2020 ). However, the rapid information processing and insightful responses provided by AI challenge traditional learning methods, raising questions about the distinctions between human learning and machine-based learning. For example, while AI can efficiently process and analyze data, it may lack the nuanced understanding and creativity inherent in human cognition ( Luckin et al., 2016 ). This underscores the need for a balanced approach to AI integration, ensuring that it complements rather than replaces human interaction and the development of critical thinking skills ( Wu, 2023 ).
The analysis was conducted at two levels as follows: vertical analysis, which focused on individual responses to identify unique insights; and horizontal analysis, which examined patterns across the entire dataset to ensure a comprehensive understanding of emerging themes. To enhance the validity and reliability of the findings, the analysis adhered to a structured, iterative process, as outlined by Ezzy 2002 ). This process included the following: transcription of participants’ responses, creation of a coding scheme; development of an analysis grid; analysis of transcripts and thematic interpretation; and calculation of statistical indicators. To further strengthen the validity of the results, data triangulation was employed. Responses from students in the Aerospace and Medical Engineering programs were compared to identify potential program-specific trends and to validate the consistency of the identified themes across different student populations. This mixed-methods approach, integrating both qualitative and quantitative analysis, provided a rich and nuanced understanding of students’ perceptions of AI in education, while ensuring the reliability, depth, and generalizability of the findings.
Quantitative data were analyzed using descriptive statistical methods, including frequency and percentage calculations, to provide a clear overview of students’ perceptions, usage patterns, and the perceived effectiveness of AI tools. For the qualitative data, responses to the open-ended questions were analyzed using thematic analysis, a well-established and rigorous method for interpreting qualitative data. The thematic analysis followed the framework proposed by Braun and Clarke 2006 ), ensuring a systematic and transparent approach to identifying, analyzing, and reporting patterns (themes) within the data.
Data were collected using a self-administered questionnaire distributed via Google Forms. This platform was chosen for its user-friendly interface and integrated analytical tools, which facilitated efficient data management and interpretation. The study utilized a mixed-methods design, incorporating both quantitative and qualitative data collection techniques. The questionnaire consisted of 11 items, categorized into two types of questions as follows; 7 closed-ended questions designed to quantify students’ responses and identify patterns, and 4 open-ended questions aimed at capturing detailed qualitative insights into participants’ experiences, perceptions, and expectations regarding the impact of AI on their learning. The closed-ended questions were further divided as follows: yes/no questions (Items 1, 4, and 6), multiple-choice questions (Items 2 and 7), and Likert scale questions (Items 3 and 5), enabling both categorical and ordinal data analysis.
This approach allowed for a focused examination of individuals who had direct exposure to the technologies central to the study. Given the small sample size and its focus on specific academic programs, the findings should be considered preliminary and exploratory. They cannot be generalized to all students within these programs or to those in other fields. Future research with a larger, more diverse sample is needed to provide more robust insights.
The sample consisted of 85 second-year students, purposefully selected from the Aerospace and Medical Engineering programs, which emphasize AI integration. A purposive, non-probabilistic sampling method was employed to ensure the selection of participants with direct experience in AI-integrated learning environments, thus enhancing the relevance of the findings. Second-year students were chosen due to their foundational knowledge and early exposure to advanced technologies, such as AI.
3. Results
The data interpretation involved addressing each research question both quantitatively, by calculating frequencies within specific thematic categories; and qualitatively, through the statistical analysis of the participants’ responses.
According to the statistical data obtained, 95.6% of respondents use artificial intelligence technologies in academic activities. This high percentage suggests the widespread adoption of AI tools among students, reflecting the increasing integration of advanced technologies into the educational landscape.
Regarding the main types of AI used in academic activities, 88.2% of respondents use virtual assistants (e.g., ChatGPT, Siri, Google Assistant, etc.), 42.4% of respondents use AI-based educational platforms (e.g., Coursera, Duolingo, etc.), 17.6% of respondents use automatic content generation tools, 8.2% of respondents use data processing tools (e.g., predictive analysis), while 3.5% of respondents use other types of AI. For the data analysis, frequency analysis and percentage calculations were applied. These descriptive techniques allowed for the determination of the distribution of responses across each AI usage category. Percentages were calculated by relating the number of responses for each category to the total number of participants (100% of responses), providing a clear picture of the prevalence of each AI usage option. Frequency analysis was essential in identifying general trends and understanding which technologies are the most and least popular in the academic environment. The results of the analysis suggest that AI usage in academia is already a well-established practice, with a clear preference for virtual assistants and AI-based educational platforms. This reflects a global trend towards the integration of interactive and accessible technologies in the educational process. Virtual assistants, being easy to use and accessible for a wide range of academic tasks, are by far the most popular. Additionally, AI-assisted educational platforms provide a personalized learning experience, which contributes to their frequent use.
Furthermore, we set out to investigate the frequency of artificial intelligence tool usage in academic activities. The results show significant variation among students, with data indicating the widespread adoption of these technologies. Most students (57.6%) use them weekly, suggesting that these tools have become an integral part of the educational process, assisting with homework, projects, and knowledge enhancement. A considerable percentage, 18.8%, use AI daily, indicating a higher reliance on these technologies, possibly because they consider them essential for learning—whether through virtual assistants, educational platforms, or other AI-based tools. On the other hand, 11.8% of students use AI monthly, which may suggest occasional use depending on academic needs, while a similar percentage (11.8%) uses them rarely, indicating limited adoption or a preference for traditional learning methods. Only 1.2% of students stated that they do not use AI tools at all, confirming that AI has become an almost indispensable resource in education.
We also wanted to investigate students’ perception of the impact of artificial intelligence (AI) use in their learning process. The results showed a significant majority of students believe AI is helpful in their academic activities. Thus, 80% of them agree that using AI-based technologies enhances their educational experience, either by optimizing the time spent studying or by providing quick access to personalized educational resources that facilitate their understanding of complex materials. However, 17.6% of students expressed uncertainty about the effects of AI on their learning, which may suggest either limited use or a lack of familiarity with the potential of these tools. This group may include students who have not explored all the options AI offers in detail or those who are not yet convinced of the effectiveness of these technologies. In contrast, only 2.4% of students considered AI to be of no benefit to their educational process. These students might prefer traditional learning methods or may face difficulties in using these technologies, limiting their understanding of AI’ s potential in supporting their studies.
Regarding the impact of AI usage on academic performance in exams, projects, and grades, many students believe that AI plays a positive role in improving their results. Specifically, 82.4% of students think that using AI contributes to enhancing their academic performance. However, some (15.3%) believe that implementing AI does not bring significant changes in this regard, and 3.5% feel that it only limits knowledge acquisition. Additionally, only 2.4% of students think that using AI could lead to a decline in academic performance, suggesting that there are also critical voices regarding the long-term effects of this technology on the educational process.
We also wanted to investigate to what extent students believe that the use of artificial intelligence (AI) contributes to their efficiency in the learning process, considering aspects such as saving time or quick access to relevant information. The results show that most students (83.5%) believe that AI improves their learning efficiency by facilitating quick access to educational resources, reducing the time needed to find information, and helping them organize their academic activities better. On the other hand, 10.6% of students do not think that using AI increases their learning efficiency, which may suggest a different perception of the utility of these technologies or a lack of familiarity with their potential. Additionally, 7.1% of students stated that they are unsure about AI’ s impact on their learning efficiency, which could reflect less frequent use or uncertainty about how these tools can support their learning. Most students recognize the benefits of AI in enhancing learning efficiency; however, there is still a portion of them who are not convinced by its effects, highlighting the need for the better understanding and use of these technologies.
When it comes to the main concerns students have about using artificial intelligence in education, the most significant issue is the possibility of receiving incorrect or imprecise answers, with 48.2% of students expressing this concern. This is closely followed by worries about the negative impact on critical thinking (16.5%) and the risk of becoming overly dependent on technology (16.5%). Other concerns, though less common, include data privacy issues (9.4%), the fear that the results generated by AI may not truly belong to the student (3.5%), and a category labeled as “other” (5.9%). These concerns reflect a strong awareness among students of both the potential benefits and drawbacks of AI in education, particularly in relation to the accuracy of AI-generated content and its possible effects on their cognitive abilities and autonomy.
For the item “What suggestions do you have for improving the use of AI in education to more effectively support the learning process?”, we applied thematic analysis to identify recurring patterns in student responses. This analysis followed a systematic process to ensure validity and reliability in capturing key themes. The following four our thematic categories were identified ( Table 1 ), reflecting students’ varied perspectives and concerns regarding the use of AI in education: (1) Proper Integration of AI in Educational Activities (27 responses): Students emphasized the need to strategically embed AI tools into teaching practices and learning environments, ensuring they complement rather than disrupt traditional methods. Suggestions included personalized learning platforms, instant feedback applications, and algorithms to detect knowledge gaps and recommend exercises. (2) Limited and Controlled Use of AI (14 responses): Students advocated for a balanced approach, where AI supports learning without replacing human interaction or critical thinking. Concerns about over-reliance on AI and the need for clear usage guidelines were highlighted. (3) Improved Accuracy of Information (14 responses): Students stressed the importance of reliable AI-generated content, calling for rigorous validation processes to ensure accuracy and avoid misinformation. (4) Others (24 responses): This category included diverse suggestions, such as user-friendly AI interfaces, transparency in AI decision-making, and requests for free or discounted AI tools.
To strengthen the validity and reliability of our findings, we employed data triangulation by comparing responses across different academic programs, namely Aerospace and Medical Engineering students. This allowed us to identify program-specific trends or concerns and cross-verify the consistency of the themes across different student populations. Triangulation further supported the robustness of our findings by ensuring that the identified themes held true across diverse student groups. The findings indicate that while students recognize the potential of AI to improve learning, they also have concerns and suggestions for optimizing its use in education. Overall, students advocate for a thoughtful and responsible approach to AI integration, emphasizing its potential to enhance the learning process when used in a balanced and well-regulated manner. The analysis of these themes highlights the importance of ensuring AI tools add value to education without compromising the integrity of traditional learning methods.
For the item “What are the main ways in which the use of artificial intelligence enhances your learning process? (e.g., helps with understanding materials, saves time, provides additional resources, etc.)”, we identified three thematic categories as follows ( Table 2 ): (1) Timesaving, (42 responses): Students widely noted that AI tools reduce the time spent on tasks like researching, processing information, and automating repetitive activities. AI’ s ability to provide summaries, find relevant information, and perform complex calculations allows learners to focus more on critical thinking and understanding. (2) Optimization of Information Comprehension (24 responses): Participants highlighted AI tools, such as intelligent tutoring systems, for simplifying complex concepts and presenting information in digestible formats. Tailored content, instant feedback, and adaptive learning enhance understanding, retention, and engagement. (3) Information Structuring (16 responses): Students emphasized AI’ s role in organizing and categorizing information effectively. AI aids in visualizing data, creating outlines, and structuring research, helping learners navigate large volumes of information and focus on key aspects.
To strengthen the robustness and validity of these findings, descriptive statistical methods were applied to determine the frequency of responses within each thematic category, providing a quantitative dimension to support the qualitative analysis. These steps contributed to a comprehensive understanding of AI’ s role in enhancing the learning process. The findings highlight the multiple ways in which AI is integrated into learning, improving both efficiency and depth of understanding. The responses underscore AI’ s significant impact, not only in saving time but also in enhancing comprehension and aiding information organization, all of which contribute to a more effective and personalized learning experience.
For the item “What are the main challenges or limitations you encounter when using artificial intelligence for your studies? (e.g., incorrect information, excessive dependence on technology, lack of personalization, etc.)”, we identified the following four thematic categories ( Table 3 ): (1) Accuracy of information, (60 responses): The primary concern among users is the reliability of data provided by AI tools. Incorrect or outdated information can lead to misunderstandings and may negatively impact the learning process. (2) Over-dependence on Technology (7 responses): Students highlighted the risk of relying too heavily on AI, which may hinder critical thinking and independent problem-solving skills. (3) Various Errors (6 responses): This category includes minor mistakes or glitches in AI systems that can disrupt the learning experience. (4) Others (6 responses): This category captures additional challenges that do not fit into the primary themes, reflecting the diverse limitations encountered by users in different contexts.
To complement the qualitative analysis, descriptive statistical methods were employed to calculate the frequency of responses in each category, providing a quantitative basis for the identified themes. This triangulation of data (i.e., combining qualitative thematic analysis with quantitative frequency counts) strengthens the validity of the findings by offering a comprehensive overview of students’ concerns regarding AI usage in education. These findings emphasize the need for ongoing efforts to address the challenges associated with AI integration into education. The concerns highlighted in the responses underline the importance of ensuring the accuracy, reliability, and ethical deployment of AI tools to mitigate the risks of over-reliance and system errors, ultimately fostering an environment where AI can complement and enhance traditional learning methods.
For the item “How would you describe the impact of using artificial intelligence on the way you learn and collaborate with classmates or professors?”, we identified three thematic categories as follows ( Table 4 ): (1) Positive impact (51 responses): A significant number of respondents viewed AI as a valuable tool for enhancing learning and collaboration. AI was seen as facilitating access to resources, providing personalized content, and improving communication with peers and instructors. Tools like intelligent tutoring systems, discussion platforms, and automated feedback mechanisms were noted for increasing efficiency, engagement, and streamlining interactions. (2) Negative Impact (15 responses): Some participants experienced challenges, such as over-reliance on AI reducing critical thinking, a lack of human interaction, and concerns about the accuracy or suitability of AI-generated content. For some, AI-driven collaborations created a sense of detachment, diminishing the depth of their learning experience. (3) Neutral Impact (15 responses): Some participants perceived no significant shift in their learning or collaboration due to AI. They found AI tools useful but not transformative, suggesting that AI supplements rather than fundamentally alters their academic interactions.
Descriptive statistical methods were used to calculate the frequency distributions for each category, providing a quantitative representation of AI’ s perceived impact. These findings align with qualitative data, where most respondents emphasized AI’ s potential to enhance learning and collaboration. However, the negative and neutral categories highlight areas for improvement, such as ensuring accuracy, fostering human interaction, and promoting critical thinking.
In conclusion, the thematic analysis reveals a generally positive outlook on AI’ s integration into academic environments, with most respondents reporting enhanced learning experiences. However, the findings also underscore the need for caution, as challenges like over-reliance and reduced human interaction must be addressed to optimize AI’ s role in education.
| 2025-03-14T00:00:00 |
2025/03/14
|
https://www.mdpi.com/2227-7102/15/3/343
|
[
{
"date": "2022/12/01",
"position": 56,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
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"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
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"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 60,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 54,
"query": "artificial intelligence education"
},
{
"date": "2025/02/01",
"position": 67,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 51,
"query": "artificial intelligence education"
}
] |
Microsoft, OpenAI, and a US Teachers' Union Are Hatching a Plan to ...
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The National Academy for AI Instruction will make artificial intelligence training accessible to educators across the country, according to ...
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| 2022-12-01T00:00:00 |
https://www.reddit.com/r/artificial/comments/1lumzge/microsoft_openai_and_a_us_teachers_union_are/
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[
{
"date": "2022/12/01",
"position": 57,
"query": "artificial intelligence education"
}
] |
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Survey: 60% of Teachers Used AI This Year and Saved up to 6 ...
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Survey: 60% of Teachers Used AI This Year and Saved up to 6 Hours of Work a Week
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https://www.the74million.org
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[
"Lauren Wagner"
] |
Researchers have found that AI education tools can be incorrect and biased — even scoring academic assignments lower for Asian students than for ...
|
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Nearly two-thirds of teachers utilized artificial intelligence this past school year, and weekly users saved almost six hours of work per week, according to a recently released Gallup survey. But 28% of teachers still oppose AI tools in the classroom.
The poll, published by the research firm and the Walton Family Foundation, includes perspectives from 2,232 U.S. public school teachers.
“[The results] reflect a keen understanding on the part of teachers that this is a technology that is here, and it’s here to stay,” said Zach Hrynowski, a Gallup research director. “It’s never going to mean that students are always going to be taught by artificial intelligence and teachers are going to take a backseat. But I do like that they’re testing the waters and seeing how they can start integrating it and augmenting their teaching activities rather than replacing them.”
At least once a month, 37% of educators take advantage of tools to prepare to teach, including creating worksheets, modifying materials to meet student needs, doing administrative work and making assessments, the survey found. Less common uses include grading, providing one-on-one instruction and analyzing student data.
A 2023 study from the RAND Corp. found the most common AI tools used by teachers include virtual learning platforms, like Google Classroom, and adaptive learning systems, like i-Ready or the Khan Academy. Educators also used chatbots, automated grading tools and lesson plan generators.
Most teachers who use AI tools say they help improve the quality of their work, according to the Gallup survey. About 61% said they receive better insights about student learning or achievement data, while 57% said the tools help improve their grading and student feedback.
Nearly 60% of teachers agreed that AI improves the accessibility of learning materials for students with disabilities. For example, some kids use text-to-speech devices or translators.
More teachers in the Gallup survey agreed on AI’s risks for students versus its opportunities. Roughly a third said students using AI tools weekly would increase their grades, motivation, preparation for jobs in the future and engagement in class. But 57% said it would decrease students’ independent thinking, and 52% said it would decrease critical thinking. Nearly half said it would decrease student persistence in solving problems, ability to build meaningful relationships and resilience for overcoming challenges.
In 2023, the U.S. Department of Education published a report recommending the creation of standards to govern the use of AI.
“Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are well-aware of ‘teachable moments’ that a human teacher can address but are undetected or misunderstood by AI models,” the report said. “Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in ed tech.”
Researchers have found that AI education tools can be incorrect and biased — even scoring academic assignments lower for Asian students than for classmates of any other race.
Hrynowski said teachers are seeking guidance from their schools about how they can use AI. While many are getting used to setting boundaries for their students, they don’t know in what capacity they can use AI tools to improve their jobs.
The survey found that 19% of teachers are employed at schools with an AI policy. During the 2024-25 school year, 68% of those surveyed said they didn’t receive training on how to use AI tools. Roughly half of them taught themselves how to use it.
“There aren’t very many buildings or districts that are giving really clear instructions, and we kind of see that hindering the adoption and use among both students and teachers,” Hrynowski said. “We probably need to start looking at having a more systematic approach to laying down the ground rules and establishing where you can, can’t, should or should not, use AI In the classroom.”
Disclosure: Walton Family Foundation provides financial support to The 74.
| 2022-12-01T00:00:00 |
https://www.the74million.org/article/survey-60-of-teachers-used-ai-this-year-and-saved-up-to-6-hours-of-work-a-week/
|
[
{
"date": "2022/12/01",
"position": 65,
"query": "artificial intelligence education"
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"date": "2023/02/01",
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"query": "future of work AI"
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"query": "future of work AI"
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{
"date": "2024/07/01",
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"query": "future of work AI"
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"date": "2024/09/01",
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"query": "artificial intelligence education"
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{
"date": "2025/06/01",
"position": 67,
"query": "artificial intelligence education"
}
] |
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AI Literacy is Imperative for Classroom Success
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AI Literacy is Imperative for Classroom Success
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https://www.tc.columbia.edu
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[] |
Pictured: Irina Lyublinskaya, Professor of Mathematics & Education (left) and Xiaoxue Du (Ed.D. '22) (right). “AI literacy has the potential to ...
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Teachers College Building Teaching AI Literacy Across the Curriculum by TC’s Irina Lyublinskaya and Xiaoxue Du (Ed.D. ’22). (Photo courtesy of Corwin Press)
As the world becomes increasingly technology-driven with artificial intelligence (AI) leading the way, equipping students with AI literacy skills is essential. A report from the World Economic Forum (WEF) predicts that AI will create 170 million new roles over the next decade. This shift underscores the need for students to grasp AI's role in the job market, preparing them for a future where AI literacy is crucial for career success. Beyond conventional teaching methods, educators are tasked with helping students grasp the intricacies of AI—its functionality, potential applications and even inherent limitations.
In their new book, Teaching AI Literacy Across the Curriculum, TC alumnus Xiaoxue Du (Ed.D. ’22) and Irina Lyublinskaya, Professor of Mathematics & Education, delve into the importance of AI literacy, providing educators with insights into the complexities of artificial intelligence while offering practical strategies for seamlessly integrating AI into a multidisciplinary curriculum.
Pictured: Irina Lyublinskaya, Professor of Mathematics & Education (left) and Xiaoxue Du (Ed.D. ’22) (right).
“AI literacy has the potential to spark curiosity, boost critical thinking, and foster a love of lifelong learning, but it all comes down to how we, as educators, choose to integrate it,” shares Lyublinskaya and Du, who impart insights to help teachers leverage AI seamlessly into their curricula.
Explore key takeaways from the book below.
1. Understanding AI’s Multidimensional Complexities
(Photo: iStock)
Rather than simply familiarizing oneself with the technical intricacies of artificial intelligence, AI literacy requires a deeper understanding of its ethical, societal and cultural implications. “Teachers are central to the success of AI literacy education,” explains Lyublinskaya, who currently teaches a course for educators focused on AI literacy. “We are the ones who introduce students to AI concepts, guide ethical discussions and help students apply AI to real-world problems.
The book emphasizes that students who comprehend the underlying mechanisms of AI and the potential biases associated with it are more likely to question unjust outcomes and, in return, generate innovative solutions. Ensuring that all students are AI literate before applying their skills is crucial. “AI literacy can both empower and limit decision-making processes, depending on how they are designed and deployed— a critical understanding of AI’s impact is essential for students to navigate the future of AI development and its societal integration.”
2. Embrace a Multidisciplinary Approach
(Photo: iStock)
Lyublinskaya and Du emphasize that AI should not be simply confined to computer science courses. Instead, educators should use a multidisciplinary lens when integrating artificial intelligence into their existing curriculum. The book’s pedagogical framework suggests embedding AI into core subjects, like mathematics, science, social studies and the language arts, utilizing real-world scenarios and the “Five Big Ideas” in AI: perception, representation and reasoning, learning, natural interaction and societal impact.
For instance, educators might experiment with using AI-generated data in math, engage students in debates about AI ethics when learning about genetic engineering, or use AI-powered chatbots to tell immigration stories, keeping in mind that there's no need to start from scratch; instead, they should embed AI literacy into existing teaching practices. “By encouraging exploration, experimentation, and problem-solving in authentic, existing contexts, educators can deepen students' understanding of AI's influence on their lives and society,” explain the coauthors.
3. Teachers Need Support
(Photo: iStock)
Teachers are central to AI literacy, but often lack training and resources. Research indicates that collaborative learning communities and mentorship programs have been identified as “effective means of facilitating knowledge exchange” and “skill development among educators.” “AI requires a specific set of skills and knowledge that many of us as educators may not yet possess,” explain Lyublinskaya and Du. “This creates a global need for professional development and training that equips teachers with both the technical expertise and the pedagogical strategies to integrate AI literacy into their classrooms.” The book offers resources to develop teachers' AI literacy and practical tools to support integration of AI literacy into their classrooms.
Additionally, the book emphasizes that it’s essential to create a shared learning community among teachers that includes diverse perspectives, which better enhances the effectiveness of professional development and continuous learning. “By incorporating different perspectives across computer science, education, and ethics, teachers are better prepared to navigate the complexities of AI integration in diverse educational settings.”
— Jacqueline Teschon
| 2022-12-01T00:00:00 |
https://www.tc.columbia.edu/articles/2025/july/ai-literacy-is-imperative-for-classroom-success/
|
[
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"date": "2022/12/01",
"position": 71,
"query": "artificial intelligence education"
},
{
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"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
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"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 77,
"query": "artificial intelligence education"
}
] |
|
Free Artificial Intelligence Course for Students - IBM SkillsBuild
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Free Artificial Intelligence Course for Students
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https://skillsbuild.org
|
[] |
Learn artificial intelligence with this basic AI course and find how this AI resource will positively impact your career. Click here to learn more.
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You've probably heard about the artificial intelligence and machine learning algorithms that power your favorite apps with data science and deep learning techniques. But how much do you really know about how AI works or how it's changing the world around us? Learn the basics of this technology, which has the potential to change every single job in the near future, and start building your skills with these free courses.
| 2022-12-01T00:00:00 |
https://skillsbuild.org/students/course-catalog/artificial-intelligence
|
[
{
"date": "2022/12/01",
"position": 72,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
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"query": "artificial intelligence education"
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{
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"query": "artificial intelligence education"
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{
"date": "2024/09/01",
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"query": "artificial intelligence education"
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{
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"query": "artificial intelligence education"
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{
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"position": 92,
"query": "artificial intelligence education"
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{
"date": "2025/06/01",
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"query": "artificial intelligence education"
}
] |
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Bachelor of Science (Honours) in Artificial Intelligence and ...
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Bachelor of Science (Honours) in Artificial Intelligence and Educational Technology
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https://www.apply.eduhk.hk
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[] |
The BSc(AI&EdTech) programme is designed to meet the growing societal demand for professionals in artificial intelligence and educational technology.
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Programme Short Name Study Mode Normal Period of Study JUPAS Code EdUHK Programme Code BSc(AI&EdTech) Full-time 4 Years (Year 1 Admissions)
2 Years (Senior Year Admissions) JS8714 A4B095
Programme Introduction The BSc(AI&EdTech) programme is designed to meet the growing societal demand for professionals in artificial intelligence and educational technology, while actively responding to the continuous evolution of education and other industries in the era of digitalization and intelligence. It aims to: provide students with fundamental knowledge and skills in artificial intelligence and educational technology; develop students’ ability in applying knowledge of artificial intelligence and educational technology appropriate to teaching and learning; build up students’ skills in using appropriate methods of artificial intelligence and educational technology to approach and solve real-world problems in educational contexts; and equip students with the capacity to conduct and evaluate educational projects supported by ethical use of artificial intelligence and educational technology.
Programme Features Industry-Relevant Skills Equips students with technical knowledge in AI, including machine learning, neural networks, natural language processing, computer vision, and data science, along with essential skills for diverse career paths Interdisciplinary Focus Integrates AI with educational technology, preparing students for careers at the intersection of these fields Hands-on Learning Emphasises practical experience through internships, projects, and lab work, enabling students to apply their skills in real-world settings
Programme Structure Domain Credit Points (cps) Year 1 Admissions Senior Year Admissions Major - Major Core 33 15 - Major Electives 6 6 - Major Interdisciplinary Course 3 3 - Living and Working in Our Country 3 / - Cross-Faculty Core Course 3 3 - Internship 6 6 Final Year Project (Honours Project / Capstone Project) 6 6 Second Major / Minor / Electives 30 15 General Education 22 6 Language Enhancement 9 / Total: 121 60 Notes:
(1) Classes will be held in Tai Po Campus and Tseung Kwan O Study Centre / North Point Study Centre / Sports Centre / Kowloon Tong Satellite Study Centre as decided by the University.
(2) Students admitted into this programme are required to visit the Greater Bay Area (GBA) and/or other parts of Mainland China. The programme may also require students to participate in other non-local learning experience for completion of the programme. While the visits are subsidised, students are required to contribute part of the estimated cost of the visits ("students’ contribution"), whereas any personal entertainment, meals expenses, travel document fee and personal insurance costs shall be at students’ own expense. The estimated cost of the visits and students’ contribution for students admitted to the coming cohort is yet to be available due to a variety of factors such as inflation of cost of the visits, trip duration, traveling expenses, the exchange rate, etc.
Internship / Overseas Study Opportunities A unique feature of the programme is the inclusion of internships at technology companies and educational organisations. The internship provides students with hands-on experience, enabling them to apply their knowledge and skills in key areas such as machine learning, natural language processing, computer vision, educational technology, and data analytics. Through this opportunity, students will tackle practical challenges, build professional networks, and gain insights into industry operations. This immersive experience enriches their knowledge and provides a solid foundation for their future careers in artificial intelligence and educational technology.
Career Prospects / Professional Recognition Graduates of this programme will be well-prepared for technical and support roles across various sectors, such as AI engineers, data scientists, software engineers, e-learning developers, educational technology specialists, and teaching assistants. Graduates can pursue roles in the IT industry, schools, educational technology companies, IT-related positions in other industries, as well as government and non-governmental organisations. With experience, graduates have the potential to advance to leadership roles such as systems analysts and educational technology managers. Additionally, the programme provides a strong foundation for graduates who are interested in pursuing postgraduate studies in artificial intelligence, education, or information technology.
| 2022-12-01T00:00:00 |
https://www.apply.eduhk.hk/ug/programmes/aiet
|
[
{
"date": "2022/12/01",
"position": 76,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 71,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 72,
"query": "artificial intelligence education"
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{
"date": "2024/12/01",
"position": 73,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 78,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence Courses | Harvard University
|
Artificial Intelligence Courses
|
https://pll.harvard.edu
|
[] |
HKS Executive Education and Harvard's John A. Paulson School of Engineering and Applied Sciences present a webinar featuring Harvard faculty Martin Wattenberg ...
|
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
| 2022-12-01T00:00:00 |
https://pll.harvard.edu/subject/artificial-intelligence
|
[
{
"date": "2022/12/01",
"position": 80,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 75,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 73,
"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 77,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 74,
"query": "artificial intelligence education"
},
{
"date": "2025/02/01",
"position": 94,
"query": "artificial intelligence education"
},
{
"date": "2025/06/01",
"position": 81,
"query": "artificial intelligence education"
}
] |
|
Pedagogical Design of K-12 Artificial Intelligence Education - MDPI
|
Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
|
https://www.mdpi.com
|
[
"Yue",
"Jong",
"Morris Siu-Yung",
"Dai",
"Miao Yue",
"Morris Siu-Yung Jong",
"Yun Dai"
] |
The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science ...
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Because of the widespread adoption of artificial intelligence (AI) technology, the world is undergoing an unprecedented technological change. From its emergence in the computer science field, AI has spread across diverse fields (e.g., engineering, business, art, and science), eventually affecting many facets of human life. The application of AI (as observed, for example, in smart home appliances, cloud services, smartphones, Google-enhanced smart speakers, and devices equipped with Siri) enhances user experience, improves working efficiency, and increases the convenience of various tasks. For effective functioning in the information era, people must develop AI literacy through the acquisition of new skills [ 1 ]. The organization for economic cooperation and development (OEDC) released a report,, highlighting the importance of equipping students with new skillsets to enable them to thrive in increasingly automated economies and societies [ 2 ]. AI can thus be considered as an essential technological literacy for the 21st century, expanding the list of classic literacies such as digital literacy, data literacy, and information literacy [ 3 ]. Having AI literacy may encourage more students to consider AI careers and provide solid preparation for higher education and their future career. To empower students with AI literacy, an AI education ecosystem that covers all educational stages, not only the graduate and undergraduate levels, should be established [ 4 5 ]; that is, more focus should be placed on the K-12 context. In 2019, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) encouraged the exploration of the curriculum and standard dimensions of AI in K-12 education to elucidate how learners and teachers are preparing for an AI-powered world [ 6 ]. Therefore, the practical movement to integrate AI in K-12 education has been observed in various countries in recent years (e.g., the United States, the United Kingdom, Finland, China, Australia, and South Korea). Moreover, in academia, the discussion on AI education has steadily shifted from higher education to the K-12 context as well. An increasing number of studies have explored the potential of incorporating AI learning into K-12 education through playful experiences and approachable content to prepare children for an AI-saturated world and future AI-oriented workforces [ 7 8 ].
Initiatives to popularize a basic understanding of AI technologies in K-12 have been emphasized both in practice and theories. However, a systematic analysis of the approaches used to equip students with the knowledge of AI technology in K-12 classrooms is lacking. The aims of this paper are to characterize, compare, and synthesize the characteristics of the design and implementation of AI courses on the basis of current research. This paper provides an overview of pertinent constructs in AI teaching and learning (T&L) as well as identifies potential gaps and opportunities for future research.
Because of its interdisciplinary nature, K-12 AI education can be regarded as emerging from computer science education, with the integration of technology education, engineering education, and knowledge from other fields. The fact that AI is a mixture of various disciplines brings about the challenge of scoping AI in the K-12 context. However, consensus has yet to be reached on the specific content of K-12 AI education. At the undergraduate and higher levels, traditional AI education focuses on teaching algorithms and their background, but for the K-12 audience, the boundaries of AI education seem to be broader. K-12 AI education emphasizes not only the technical functioning of computers but also its social construction process related to the knowledge of technology, engineering, science, even humanity, and sociology [ 4 7 ].
The field of technology and engineering education is closely related to AI education, in which constructivism is popular in T&L. The constructivist learning theory, commonly adopted in technology and engineering education, is based on the developmental theories of Piaget [ 10 ], with further elaboration by Vygotsky [ 11 ]. According to the cognitive constructivist theory, students construct the meaning of knowledge from experience [ 10 ], and Vygotsky emphasized the key role of sociocultural factors in students’ constructive learning process [ 11 ]. Pedagogical approaches founded on constructivist theories prioritize active participation and deep learning through inquiry-based, project-based, problem-based, and discovery-based activities [ 12 ]. In K-12 technology and engineering education, students are guided to establish a connection between their experience and new knowledge in the sociocultural environment, thus promoting conceptual construction when students encounter new technological or engineering concepts that conflict with their prior knowledge [ 13 ]. That is, students are encouraged to negotiate with their experience and interact with the learning community through constructivist activities to shape their conceptual frameworks and improve their literacy [ 14 15 ]. In technology and engineering classes, students are oftentimes required to complete a final product following a lesson; for example, designing artifacts or step-by-step procedures for specific tasks.
In computer science, AI is defined as any human-like intelligence exhibited by a computer, robot, or other machines; that is, AI refers to the ability of a computer or machine to imitate the capabilities of the human mind—learning from experience and examples, recognizing objects, understanding and responding to language, making decisions, solving problems, and combining these capabilities to perform functions typically attributed to humans [ 3 ]. AI knowledge originally centered on topics such as algorithms, coding, and programming in computer science courses; AI education was then gradually introduced in higher education and later also in basic education. In primary and secondary schools, AI, which was initially covered as a relevant part of computational thinking (CT) curricula, was included in science, technology, engineering, the arts, and mathematics (commonly referred to as STEAM) education [ 9 ]. CT curricula aim to cultivate young students’ competencies in solving problems through the use of programming, with the intention of preparing them for their subsequent tertiary studies and their future careers in computer science. The increasing application of AI technology powered by programming highlights the importance of introducing fundamental AI knowledge and working principles to students throughout K-12.
AI education evolved from the field of computational science at the college level. Initially, computer scientists designed and developed AI technology and integrated it into computer science classrooms. Because the development of AI technology has substantially affected people’s lives, AI education has been extended to the K-12 context and discussed as a specialized curriculum. Furthermore, AI education is an area spanning diverse disciplines, especially technology education and engineering education.
1.2. Existing Reviews of K-12 AI Education
Although AI teaching initiatives in K-12 date back to the 1970s [ 16 ], AI teaching has grown tremendously in popularity in the past few years [ 17 ]. UNESCO organized the Workshop on Teaching and Learning Competencies for Artificial Intelligence (AI) from an Information Access Perspective to examine the elements necessary to support teachers’ and learners’ capacity development for AI use [ 6 ]. In response to UNESCO’s call to action, various K-12 AI projects and activities have been initiated worldwide (e.g., in the United States, United Kingdom, Finland, China, Hong Kong, Singapore, South Korea, India, and Australia). Governments and universities globally have begun to collaborate to support the introduction of AI in K-12 settings. The teams involved in these K-12 AI projects consist of policymakers, software developers, technological experts, educators, and frontier teachers. One representative project is the AI for K-12 Working Group (AI4K12), which proposed the “Five Big Ideas” as a framework to develop guidelines for teaching AI to K-12 learners. This framework includes five main components: perception, representation and reasoning, learning, natural interaction, and societal impact [ 18 ]. Various studies focusing on the design of AI curricula for the K-12 context have adopted the Five Big Ideas as their framework.
A number of studies emerged responding to the increasing interest in AI education. Initial research on this topic focused on the incorporation of AI education into regular K-12 subjects (e.g., science, mathematics, and physics). Research in this stage explored the intersections of AI and other core K-12 subjects to facilitate integration into the classroom. With the increasing need for K-12 AI education, studies on what and how AI knowledge should be taught in K-12 have emerged within the context of specific AI-related courses. The notion of K-12 AI education has thus evolved over the years, which calls for a thorough review of relevant studies to guide future research.
An exploratory review from Zhou and colleagues provided evidence on how AI literacy guidelines have been applied in K-12 contexts [ 19 ]. In their review, K-12 AI education was defined as the integration of AI knowledge into core curricula, not as an independent course. Several design guidelines for creating an AI learning experience in K-12 were identified: student engagement, built-in scaffolding, teacher and parent involvement, equity diversity, and inclusion.
K-12 AI courses have evolved into teaching machine learning (ML), one subfield of AI, to students. Sanusi and Oyelere conducted a review to identify potential pedagogical frameworks suitable for learning ML in K-12 education [ 20 ]; they described various pedagogical tactics, such as problem-based learning, project-based learning, active learning, participatory learning, interactive learning, inquiry-based learning, personalized learning, and design-oriented learning. This provided a comprehensive overview of the pedagogical frameworks adopted in K-12 education for ML; however, a detailed analysis of how teachers employed these pedagogical frameworks in their teaching practice was absent.
Similarly, Marques et al. reviewed 30 papers in a systematic mapping study to explore how to teach ML to K-12 learners [ 17 ]. They identified 30 courses or programs in these papers and examined their characteristics. The authors observed three main features: (1) competencies can range from basic (knowing what ML is) to more complex (understanding specific ML techniques); (2) ML concepts are introduced by focusing on the most accessible processes; (3) instructional materials in addition to customized frameworks and tools are available for free. This review provided a basic understanding of current K-12 teaching practice related to ML; however, the courses or programs examined in this review focus exclusively on ML and only as part of a comprehensive AI course. Moreover, the main aim of this review focused specifically on the learning content (e.g., ML topics, concepts, processes, learning styles, application domains, frameworks, and data types) but neglected to provide a fine-grained description of how the instructional design is developed.
With the development of K-12 AI courses, new tools for teaching AI in schools emerge regularly. Therefore, with reference to the review of Marques et al. [ 17 ], Gresse von Wangenheim and colleagues further investigated the key role of visual tools in teaching ML by reviewing 16 papers [ 21 ]. Their findings indicated that these visual tools provided students with opportunities to construct a comprehensive ML process, thus helping them to develop a more accurate understanding of ML concepts. The authors also highlighted the lack of collaborative learning during the development of such ML models and the dearth of performance-based assessment of these created ML models as key emerging pedagogical concerns in teaching AI knowledge by visual tools.
The aforementioned reviews explored the characteristics of teaching ML to K-12 learners; however, because ML is only one part of the AI field, these studies failed to provide a comprehensive picture of the AI technology landscape. The review conducted by Su et al. presented a delineation of educational approaches for teaching AI technologies at the K-12 level in the Asia-Pacific region [ 22 ]. In that review, K-12 AI education was defined as an independent curriculum providing comprehensive knowledge. By mapping current AI tools, AI activities, educational models or theories, and research outcomes, the review outlined the status of AI curricula developed for K-12 classrooms. The findings of this review indicated that these AI curricula had a positive influence on students’ learning outcomes, interest in AI courses, AI-related skills, and learning attitudes. Nevertheless, the studies covered in that review were conducted only in Asian regions. In response to increasing research attention on the K-12 AI education field, an updated review of studies on the characteristics of K-12 AI education worldwide is necessary.
Relevant review papers have elucidated the design and development of K-12 AI courses from diverse research foci, such as AI knowledge to be taught, instructional strategies to be used in the pedagogical process, multimedia tools to be adopted to support learning visualization, etc. Nevertheless, because of the varied perspectives of these studies, an in-depth and full-scale understanding of the pedagogical characteristics of K-12 AI education remains elusive. Some of the limitations of the aforementioned reviews are as follows: (1) they included only a few relevant studies for review and analysis; (2) they lacked a strict search process to ensure the quality of reviewed papers; (3) there was a lack of quantitative description in selected papers; (4) they recognized AI courses as the integration with other subjects, not developing as an independent course; (5) they regarded ML as the only relevant knowledge and neglected other parts of AI technology necessary to be introduced in K-12 classrooms; (6) they identified teaching approaches in K-12 AI courses but lacked elaboration on how to organize and implement these approaches; (7) they verified the effectiveness of existing teaching approaches for AI knowledge but considered only some world regions.
Considering the aforementioned limitations, the purpose of this study was twofold: (1) to systematically review high-quality empirical research focusing on K-12 AI courses or programs in the world range, and (2) to explore future research orientations in terms of the pedagogical design and implementation of K-12 AI curricula. This review study provides insights on how to design, construct, and implement AI courses for K-12 instructional designers. Furthermore, the present results may inform researchers of the evolution and improvements in the field of AI pedagogy. In this paper, we delineated effective approaches to investigating K-12 AI curricula worldwide and proposed suggestions for the inclusion of AI education in K-12.
To examine and synthesize the characteristics of AI education for K-12 students from multiple perspectives, three review questions guided our examination of the current status of relevant studies on teaching AI in K-12 settings. These three review questions (RQs) were refined using the following analysis questions (AQs):
RQ1: What is the status of research in teaching AI in K-12?
AQ1: What are the major research trends, regions, and scales?
AQ2: What are the major research methods and settings?
RQ2: What are the pedagogical characteristics of current AI teaching units?
AQ3: What is the scale (target audience, setting, duration) of the teaching unit?
AQ4: What content is selected for the teaching unit?
AQ5: What prior knowledge and skills are required for students to take the course?
AQ6: What are the pedagogical theories that guide the teaching unit? What are the pedagogical approaches and T&L activities in the teaching unit?
AQ7: What are the tools and materials used in the teaching unit?
RQ3: What are the evaluation methods and the outcome of the teaching units?
AQ8: How are students’ learning outcomes assessed?
AQ9: What are the learning outcomes of the teaching units?
| 2022-01-14T00:00:00 |
2022/01/14
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https://www.mdpi.com/2071-1050/14/23/15620
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Inspirit AI: AI High School Program taught by Stanford/MIT Alums
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Inspirit AI: AI High School Program taught by Stanford
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https://www.inspiritai.com
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[] |
Inpsirit AI Scholars is an artificial intelligence program for high school students, developed and taught by Stanford and MIT alumni and graduate students.
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Our Mission is to INSPIRE.
AI Scholars inspires curious high school students globally by exposing them to the defining technology of our times: Artificial Intelligence. AI is already present everywhere: in our voice-activated devices, smartphone face recognition systems, and autonomous vehicles. The potential to apply this technology for good is limitless.
This online program — developed and taught exclusively by a team of alumni and graduate students from Stanford, MIT, and other top universities — provides guidance on initiating AI projects, pursuing AI ventures, and preparing for college.
| 2022-12-01T00:00:00 |
https://www.inspiritai.com/
|
[
{
"date": "2022/12/01",
"position": 87,
"query": "artificial intelligence education"
}
] |
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Transformations in academic work and faculty perceptions of ...
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Transformations in academic work and faculty perceptions of artificial intelligence in higher education
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https://www.frontiersin.org
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[
"Buele",
"Centro De Investigación En Mecatrónica Y Sistemas Interactivos",
"Mist",
"Facultad De Ingenierías",
"Universidad Tecnológica Indoamérica",
"Llerena-Aguirre",
"Facultad De Ciencias Sociales Y Humanas"
] |
Technologies based on artificial intelligence are transforming teaching practices in higher education. However, many university faculty ...
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Technologies based on artificial intelligence are transforming teaching practices in higher education. However, many university faculty members still face difficulties in incorporating these tools in a critical, ethical, and pedagogically meaningful way. This review addresses the issue of limited artificial intelligence literacy among educators and the main obstacles to its adoption. The objective was to analyze the perceptions, resistance, and training needs of faculty members in the face of the growing presence of artificial intelligence in educational contexts. To this end, a narrative review was conducted, drawing on recent articles from Scopus and other academic sources, prioritizing empirical studies and reviews that explore the relationship between intelligent systems, university teaching, and the transformation of academic work. Out of 757 records initially retrieved, nine empirical studies met the inclusion criteria. The most frequently examined tools were generative artificial intelligence systems (e.g., ChatGPT), chatbots, and recommendation algorithms. Methodologically, most studies employed survey-based designs and thematic qualitative analysis. The main findings reveal a persistent ambivalence: faculty members acknowledge the usefulness of such technologies, but also express ethical concerns, technical insecurity, and fear of professional displacement. The most common barriers include lack of training, limited institutional support, and the absence of clear policies. A shift in the teaching role is observed, with greater emphasis on mediation, supervision, and critical analysis of output generated by artificial intelligence applications. Additionally, ethical debates are emerging around algorithmic transparency, data privacy, and institutional responsibility. Effective integration in higher education demands not only technical proficiency but also ethical grounding, regulatory support, and critical pedagogical development. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/H53TC.
1 Introduction
In recent years, artificial intelligence (AI) has experienced unprecedented growth, expanding into various sectors, including labor, healthcare, social dynamics, and education (Ayala-Chauvin and Avilés-Castillo, 2024). In the educational domain, it has emerged as a key driver of pedagogical innovation (Su et al., 2023). Particularly, Generative AI (GenAI) has gained prominence. This type of AI can create new content such as text, images, or code, based on patterns learned from large datasets. Its applications include process automation, personalized learning support, and assistance in assessment and academic monitoring (Zhang and Aslan, 2021; Wang et al., 2024).
Historically, the integration of digital technologies into education has been gradual, punctuated by moments of disruption, such as the rise of virtual learning environments and the proliferation of open educational resources (Yildirim et al., 2018). However, AI marks a qualitative leap by enabling algorithms to process large volumes of data and tailor educational content to individual learners’ needs (Özer, 2024).
Particularly since the COVID-19 pandemic, the surge in emerging technologies has significantly transformed teaching practices in higher education (Schön et al., 2023). AI-based tools, including GenAI platforms such as ChatGPT, Deepseek, Copilot, and MetaAI now support students and faculty by generating content, providing answers, and enabling personalized learning pathways (Schön et al., 2023). Intelligent platforms enhanced with AI have also optimized instruction through automated tutoring, assisted assessment, and adaptive interactive resources (Xia et al., 2024). Nevertheless, the integration of AI into teaching presents significant challenges. One of the most pressing issues is the need for faculty training to ensure the effective pedagogical use of these tools (Sperling et al., 2024). Many educators lack the skills required to engage with these tools.
In this rapidly evolving context, AI literacy has emerged as an essential competency. Commonly defined as the ability to understand, critically evaluate, and effectively interact with artificial intelligence systems, AI literacy is part of a broader framework of multiple literacies (Tuominen et al., 2005; Ilomäki et al., 2023). Its democratic function lies in enabling individuals from diverse fields, such as health, computing, mathematics, education, or engineering to comprehend how these technologies work and what their implications are. This emphasis places formal education at the center of the debate, highlighting the role of educators and their professional expertise in guiding responsible integration.
Ethical and social implications also demand attention. A study by Ayanwale et al. (2024), involving 529 prospective teachers, underscores the need to prepare educators for responsible use. It warns of potential errors and biases from poor implementation and highlights the dual function of AI ethics: positively predicting emotional regulation and shaping perceptions of persuasive AI, often without aligning with actual competencies. Complementing these findings, (Buele et al., 2025) note that many faculty members lack the epistemic resources to critically assess algorithmic processes, which limits their ability to mentor students on responsible use.
The large-scale collection and analysis of data raise concerns about the privacy and security of information belonging to both faculty and students (Ismail, 2025). Data ownership is often unclear, potentially falling under the control of educational institutions, AI providers, or even third parties. This lack of clarity introduces risks concerning how data is used, stored, and shared. In parallel, limited training opportunities and resistance to change remain key barriers to adoption. Notably, higher levels of anxiety have been associated with greater difficulty in adapting to intelligent tools, particularly among less digitally fluent educators (Shahid et al., 2024).
Given the accelerated emergence of these technologies and the ambivalence they generate among faculty, it becomes necessary to synthesize current evidence on how they are reshaping academic work. This narrative review explores recent literature on faculty perceptions, adoption barriers, ethical considerations, and the evolving roles of university instructors. By identifying key patterns and research gaps, the study contributes to a broader understanding of how higher education is adapting to artificial intelligence.
2 Materials and methods
2.1 Design and search strategy
This review was conducted using a systematic approach for the selection and analysis of scientific literature, focusing on the perceptions, attitudes, and barriers faced by faculty in the adoption of artificial intelligence in higher education. The literature search was carried out using scientific databases recognized for their relevance in the educational and technological fields, including Scopus, Web of Science, IEEE Xplore, ERIC, EBSCOhost, and ProQuest.
Search terms were defined to align closely with the objective of this review. The selection of keywords: “faculty attitudes,” “teacher perceptions,” “teacher barriers,” “artificial intelligence,” “AI in education,” and “higher education” was based on their recurrence in previous studies and relevance to the intersection of AI and academic work in higher education. Boolean operators were applied to structure the search queries. No publication year limits were set in the search strategy.
2.2 Inclusion and exclusion criteria
To ensure the relevance of the selected studies, the following inclusion criteria were applied: (i) studies addressing the relationship between artificial intelligence and the transformation of academic work in higher education; (ii) empirical research with a solid methodological foundation; (iii) studies analyzing changes in work structure, decision-making processes, or regulation of artificial intelligence use in teaching; (iv) publications written in English.
Exclusion criteria included: (i) studies focused exclusively on students or on pedagogical uses of artificial intelligence without considering its impact on faculty; (ii) use of artificial intelligence without evaluating its effects on teaching practices; (iii) research conducted at educational levels other than higher education.
2.3 Study selection process
The article selection was carried out in two phases. The first phase involved reviewing the titles and abstracts of the studies retrieved from the databases. During this phase, duplicates were removed, and studies that did not meet the inclusion criteria were discarded. This was followed by a full-text review, in which the preselected articles were thoroughly analyzed to confirm their relevance to the objectives of this study.
2.4 Data analysis
The selected articles were organized into a synthesis table that included the following information: authors, study objectives, methodology used, type of artificial intelligence examined, educational level, main findings, limitations, and implications for teaching work. Although no formal quality appraisal tools were applied (as this is a narrative review), studies were selected based on their empirical rigor and relevance to the objectives of the review.
2.5 Ethical considerations
As this review is based on previously published studies and does not involve the collection of primary data, ethical approval was not required. Nevertheless, scientific integrity was ensured through the selection of articles from reputable sources and proper acknowledgment of the original authors.
3 Results
The search process yielded a total of 757 records from six databases. After removing duplicates and screening titles and abstracts based on predefined inclusion and exclusion criteria, 104 full-text articles were assessed for eligibility. Of these, 95 were excluded for reasons such as focus on other education levels, lack of assessment of impact on faculty, methodological issues, or inaccessibility of the full text. Ultimately, 9 studies were included in the review. The selection process is summarized in Figure 1.
FIGURE 1
Figure 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow chart.
3.1 Types of artificial intelligence used in higher education
The selected studies analyzed various applications of artificial intelligence in higher education (Table 1), with a particular focus on generative tools, chatbots, and recommendation algorithms. Generative artificial intelligence was used in n = 4 studies, focusing on academic writing and teaching support (Alcántar et al., 2024; Gustilo et al., 2024; Kurtz et al., 2024; Gârdan et al., 2025). Conversational and generative chatbots were examined in n = 3 studies, with an emphasis on automated tutoring and academic assessment (Farazouli et al., 2024; Mamo et al., 2024; Merelo et al., 2024). Recommendation algorithms and data analysis were used in n = 2 studies to explore personalized learning and the optimization of teaching (Fernández-Miranda et al., 2024). Automation tools for research and teaching were evaluated in n = 1 study, exploring their impact on human resource management and academic output (Omar et al., 2024).
TABLE 1
Table 1. Characteristics of the included studies.
3.2 Faculty perceptions and barriers regarding artificial intelligence
The reviewed literature highlights a range of attitudes that faculty hold toward artificial intelligence in higher education. Positive perceptions: (n = 4) studies found that faculty recognize the potential of artificial intelligence to enhance personalized learning and administrative efficiency (Gustilo et al., 2024; Kurtz et al., 2024; Omar et al., 2024; Gârdan et al., 2025). (n = 3) studies reported that faculty view artificial intelligence as a useful tool for academic writing and assisted teaching (Alcántar et al., 2024; Mamo et al., 2024; Merelo et al., 2024).
Identified barriers: (n = 6) studies reported concerns regarding ethics and academic integrity, specifically related to plagiarism and the lack of regulatory frameworks (Alcántar et al., 2024; Farazouli et al., 2024; Fernández-Miranda et al., 2024; Gustilo et al., 2024; Mamo et al., 2024; Omar et al., 2024). (n = 3) studies noted that the lack of faculty training constitutes a significant obstacle to adoption (Kurtz et al., 2024; Merelo et al., 2024; Gârdan et al., 2025). (n = 3) studies identified resistance to change among faculty, based on the perception that artificial intelligence could replace certain teaching functions (Farazouli et al., 2024; Mamo et al., 2024; Omar et al., 2024).
3.3 Organizational impact and changes in teaching work
The reviewed literature suggests that the implementation of artificial intelligence in higher education is reshaping the structure of academic work in several ways: academic assessment and authenticity of student work: (n = 3) studies addressed how artificial intelligence is transforming the way instructors design and evaluate exams and academic assignments (Farazouli et al., 2024; Gustilo et al., 2024; Mamo et al., 2024). One study in particular (Farazouli et al., 2024) found that faculty had more difficulty identifying texts written by humans than those generated by artificial intelligence, highlighting challenges in assessing academic authenticity.
Shifts in teaching roles and task automation: (n = 3) studies emphasized that artificial intelligence can take on functions such as automated tutoring, student performance analysis, and instructional material generation (Kurtz et al., 2024; Merelo et al., 2024; Gârdan et al., 2025). (Kurtz et al., 2024; Gârdan et al., 2025) examined how instructors may reconfigure their roles, transitioning from knowledge transmitters to facilitators of learning in AI-enhanced environments. These findings suggest that artificial intelligence is not only influencing teaching methodologies but also altering how educators allocate their time and define their professional responsibilities.
3.4 Ethical and regulatory considerations
The impact of artificial intelligence in higher education extends beyond operational and methodological changes, raising important ethical and regulatory issues. (n = 5) studies addressed concerns related to data privacy and algorithmic bias in artificial intelligence tools (Alcántar et al., 2024; Fernández-Miranda et al., 2024; Gustilo et al., 2024; Mamo et al., 2024; Omar et al., 2024). (n = 3) studies noted the lack of clear regulations governing the use of artificial intelligence in teaching, which contributes to uncertainty among faculty members (Farazouli et al., 2024; Fernández-Miranda et al., 2024; Omar et al., 2024). (n = 1) study identified a gap in equitable access to artificial intelligence tools between institutions with differing levels of resources (Kurtz et al., 2024).
4 Discussion
4.1 Ambivalent perceptions and artificial intelligence literacy
One of the most consistent findings across the reviewed literature is the ambivalence in faculty perceptions of artificial intelligence. On one hand, many university instructors acknowledge the potential of these technologies to automate repetitive tasks, provide personalized feedback, and facilitate access to new educational resources. On the other hand, they express uncertainty, fear, and rejection particularly when they do not understand how artificial intelligence works or its ethical and pedagogical implications. Gustilo et al. (2024) found that many faculty members hold contradictory opinions: they value artificial intelligence for content generation but question its reliability and fear it may undermine students’ critical thinking.
To move beyond a descriptive account and toward a more robust interpretation, this ambivalence can be examined through established models of technology adoption. The Technology Acceptance Model (TAM) explains user behavior based on perceived usefulness and perceived ease of use (Davis, 1989). While faculty members may find these tools useful for instructional efficiency, they often struggle with ease of use due to limited training, which reduces their intention to adopt. Similarly, the Unified Theory of Acceptance and Use of Technology (UTAUT) highlights the influence of social expectations and the availability of institutional support (Venkatesh et al., 2003). Across the reviewed studies, the lack of peer collaboration, administrative backing, and pedagogical guidelines emerge as a critical barrier to adoption.
In this context, the concept of AI literacy becomes especially relevant. Artificial intelligence literacy should not be limited to the technical operation of tools but should also include a critical understanding of their foundations, potential, limitations, risks, and ethical frameworks. As Lin et al. (2022), note, the lack of specific didactic and technical knowledge about artificial intelligence hinders the design of sustainable learning experiences and limits educators’ ability to meaningfully integrate these tools. Furthermore, (Heyder and Posegga, 2021) propose a typology that includes three dimensions of literacy: technical, cognitive, and socio-emotional. The literature suggests that many faculty members score low across all three, limiting their engagement in institutional or curricular decisions about AI implementation.
Institutional environments also play a decisive role. The absence of structured training programs and clear experimentation spaces deepens uncertainty and stagnation. Although the literature on faculty professional development increasingly acknowledges these challenges, specific evidence targeting the higher education sector and intelligent technologies remains scarce (Chan, 2023; Kurtz et al., 2024; Walter, 2024).
Beyond institutional dynamics, contextual and demographic variables also shape the adoption of AI tools. Factors such as academic discipline, age, digital fluency, and organizational culture influence both perceived usefulness and actual use. However, most of the reviewed studies lack detailed characterization of these dimensions (Celik, 2023; Zhang, 2023; Ding et al., 2024). Adds that faculty adoption patterns are also mediated by demographic traits: younger instructors and those with prior experience in digital tools are more open to integration, whereas older faculty or those less digitally literate often exhibit skepticism or anxiety.
Recent studies have found that younger faculty members, or those with more prior experience in digital technologies, tend to adopt AI tools with greater ease and perceive them as pedagogically valuable. In contrast, older instructors or those with limited digital exposure often exhibit skepticism or require more intensive support (Chen et al., 2020). Academic rank also plays a role, with early-career faculty showing more willingness to experiment (Heyder and Posegga, 2021).
4.2 Institutional barriers and faculty resistance
National policy frameworks and institutional governance play a critical role in shaping faculty engagement with AI. Countries that have implemented clear AI strategies and ethical guidelines tend to foster more structured institutional responses, which positively affect faculty confidence and adoption (Fernández-Miranda et al., 2024; Mah and Groß, 2024). In contrast, where such frameworks are absent or poorly implemented, faculty often encounter ambiguity and lack of institutional support.
Institutional digital maturity also influences faculty attitudes. Universities with robust infrastructures and ongoing digital transformation efforts offer more consistent training opportunities, which reduce uncertainty and facilitate AI adoption (Qadhi et al., 2024). Conversely, in low-resource environments, the lack of coordination and continuity may amplify resistance.
Several studies indicate that educators perceive the introduction of artificial intelligence as a top-down technological imposition rather than a pedagogical tool (Mah and Groß, 2024). This perception leads to defensive or indifferent attitudes, especially in the absence of institutional spaces for critical reflection or continuous professional development related to artificial intelligence. In addition, (Farazouli et al., 2024) identified that implementing intelligent technologies without clear usage policies or shared ethical criteria creates an environment of ambiguity and insecurity, prompting instructors to avoid using artificial intelligence in order to protect their professional autonomy.
A critical factor is the absence of inclusive organizational models that involve faculty in techno-pedagogical decision-making. As shown by Omar et al. (2024), when artificial intelligence adoption processes exclude faculty input, feelings of exclusion, surveillance, and loss of agency are intensified. This situation is also linked to what (Bernhardt et al., 2023) describe as conflicts over symbolic and practical control in the workplace. To counter these barriers, institutions should implement bottom-up policy models that involve faculty in decision-making processes related to AI adoption. For instance, participatory workshops, co-designed pilot programs, and interdisciplinary advisory boards can help align the implementation of artificial intelligence with pedagogical goals. International examples, such as the The University of Edinburgh, 2024, Stanford University (2024) institutional initiatives (2024) provide valuable reference models for such alignment.
Resistance is not always expressed as open opposition but also as passive resistance such as non-use, minimal use, or avoidance of the more powerful features of intelligent technologies (Karataş et al., 2025). This resistance becomes more pronounced when instructors do not perceive a clear benefit to their teaching practices or feel that the learning effort required is not sufficiently rewarded (Ayanwale et al., 2022; Jatileni et al., 2024)
Another key point is the perception of replacement. Many instructors fear that extensive use of artificial intelligence may lead to a diminished value of their professional roles, particularly in assessment, feedback, or content development (Chan and Tsi, 2023). This perception has been cited as a factor contributing to technological anxiety or even professional disidentification (McGrath et al., 2023). Disciplinary cultures also shape the extent and manner in which AI is adopted. Faculty in STEM and technology-driven fields tend to exhibit greater enthusiasm and openness, whereas those in humanities or critical pedagogy domains express more skepticism, often due to concerns over epistemic integrity or automation of reflective practice (Holmes and Porayska-Pomsta, 2022).
Finally, it is essential to highlight that institutional barriers also include lack of infrastructure, insufficient technical training, and unstable or absent policies regarding the ethical use of artificial intelligence in university contexts (Gkrimpizi et al., 2023). These organizational gaps hinder informed and critical adoption and perpetuate a superficial or purely instrumental view of artificial intelligence (Zhai, 2022; Michel-Villarreal et al., 2023).
4.3 Reconfiguration of academic work
The integration of intelligent technologies in higher education not only transforms instructional tools but also brings about a structural reconfiguration of academic work. This transformation is reflected in the redefinition of roles, the displacement of traditional tasks toward automated processes, and the emergence of new professional competencies.
Recent studies, such as Kurtz et al. (2024) suggest that educators are transitioning from the role of knowledge transmitters to that of mediators, supervisors, resource curators, and providers of emotional support especially in environments where artificial intelligence systems generate content, assess assignments, or propose personalized learning pathways.
This professional shift is not without friction. The review indicates that many educators do not feel prepared to take on these new roles, as they were not part of their initial training and there are few institutional programs to support this transition (Ng et al., 2023). This creates a tension between the expectations of digital environments and faculty members’ perceived capabilities (Celik et al., 2022).
Moreover, as noted by Machado et al. (2025), faculty perceptions of workload associated with artificial intelligence vary depending on the level of automation in educational platforms. In their experiment with automated, manual, and semi-automated scenarios, instructors reported greater cognitive effort and frustration in contexts with higher levels of human control especially when technical support was lacking. This finding reveals a paradox: while artificial intelligence is promoted as a tool to ease workload, its implementation without clear support strategies may have the opposite effect, generating overload, stress, and a sense of lost control.
Simultaneously, the transformation of academic work introduces new demands for advanced digital literacy not only in technical terms, but also in interpreting and validating algorithm-generated outputs, managing adaptive systems, and making decisions in artificial intelligence-mediated environments. These tasks have become increasingly complex as current systems do not possess human-like awareness. As noted by Bouschery et al. (2023), Dwivedi et al. (2023), generative models are often specialized in specific tasks and struggle with adaptability in more complex scenarios (Lee et al., 2024).
Nonetheless, the reviewed literature suggests that this reconfiguration also presents an opportunity to redefine the purpose of academic work highlighting human interaction, pedagogical creativity, and professional judgment in contrast to the standardization of educational processes. However, for this potential to be realized, institutional spaces for dialog and policies that acknowledge and support the emerging profile of faculty are essential (Ng et al., 2023; Adzkia and Refdinal, 2024).
4.4 Ethical dimensions and institutional responsibility
The incorporation of artificial intelligence in higher education raises a series of ethical challenges that have yet to be clearly or consistently addressed by university institutions. Among the most common concerns are data privacy, algorithmic bias, lack of system transparency, and the unclear attribution of responsibility when errors or unintended consequences arise. Additionally, overreliance on AI could undermine teacher autonomy and creativity, raising concerns about the standardization of instruction and the diminishing of the human role in education (Sperling et al., 2024).
Building on these concerns, the concept of algorithmic accountability deserves further attention. This principle refers to the obligation of developers, institutions, and users to ensure that AI systems are explainable, auditable, and aligned with ethical standards, especially in environments like education where algorithmic outputs can affect learning trajectories and evaluations (Memarian and Doleck, 2023; Pawlicki et al., 2024). Equally important is faculty agency: instructors are not merely passive users of AI tools but can act as critical mediators who validate, contextualize, or even challenge algorithmic recommendations. As Buele et al. (2025) emphasize, when educators exercise intentional control over the use of generative AI, they contribute to fostering a culture of responsible innovation in academic environments.
Many instructors report feeling unprepared to deal with these ethical dilemmas, not only due to limited digital literacy but also because of the absence of clear institutional guidelines. Lin et al. (2022) show that the ethical dimension of artificial intelligence education often takes a backseat to the technical or instrumental approach that dominates many faculty training programs.
Likewise, (Alcántar et al., 2024) point to a disconnect between the rapid development of intelligent technologies in education and the normative and governance capacities of universities, leaving instructors in an ambiguous position regarding what they can or cannot do with artificial intelligence tools.
A recurring issue in the literature is algorithmic responsibility: who is accountable when an automated system makes an erroneous or discriminatory decision? How can it be ensured that these systems uphold principles of equity, inclusion, and educational justice? These questions often go unanswered in current university policies (Baker and Hawn, 2022; Salleh, 2023; Salvagno et al., 2023).
The lack of transparency in how artificial intelligence systems are designed and operate also contributes to faculty distrust. Many instructors are unaware of how models used by students, such as automated grading systems or recommendation engines—are trained or what data they process (Halaweh, 2023). His “algorithmic black box” limits the capacity to audit or question system outputs, weakening pedagogical agency (Felzmann et al., 2020; von Eschenbach, 2021; Chowdhury and Oredo, 2023).
Moreover, the ethical digital divide becomes more pronounced when only certain faculty groups, typically those with stronger technological backgrounds, possess the competencies to critically assess these systems. Others, lacking such preparation, are excluded from decision-making and pedagogical innovation (Chiu et al., 2023). This epistemic inequality has emerged as a new source of professional exclusion, yet remains underexplored in current research (Kasinidou et al., 2025; Liu, 2025).
Beyond concerns about algorithmic opacity and data governance, the implications of generative AI for academic integrity are gaining urgency. As Lo et al. (2025) observe, AI tools may enhance student engagement and improve writing quality during revisions. However, they also challenge conventional notions of authorship and originality, blurring the line between acceptable assistance and academic misconduct. These dilemmas extend to faculty as well, particularly in relation to the use of AI in preparing teaching materials, scholarly writing, or providing feedback. Addressing this ambiguity demands clear institutional policies on AI use in academic settings, including guidelines for disclosure, authorship attribution, and acceptable practices.
To translate ethical principles into practice, higher education institutions must adopt clear and adaptable policy frameworks. Recent analyses show that universities such as MIT, University College London, and the University of Edinburgh have developed institutional guidelines for the responsible use of generative AI in teaching and learning contexts (Ullah et al., 2024). These documents typically address transparency, academic integrity, authorship, and appropriate use of AI in assessment and course design. Implementing similar policies can reduce ambiguity and foster consistency in ethical standards across departments. In parallel, faculty development should be sustained and multidimensional, integrating technical, ethical, and pedagogical training. Programs focused on prompt design, bias detection, and case-based ethical reasoning are essential to promote responsible AI use in classrooms. Frameworks like the AI Literacy for Educators model (Chiu, 2024) can support faculty confidence and critical engagement.
Additionally, peer mentoring, interdisciplinary collaboration, and reflective teaching communities contribute to a culture of experimentation and pedagogical renewal. To further support innovation, institutions might consider incentives such as pilot project grants, teaching relief, or support for research dissemination. Latin American universities, in particular, could adapt these international frameworks to fit their specific socio-educational contexts, drawing on references such as the UNESCO (2021) and broader standards from International Organization for Standardization (2023), National Institute of Standards and Technology (2024), OECD (2024).
4.5 Methodological reflections and limitations
This review was conducted using a narrative approach to synthesize emerging insights on faculty perceptions of AI in higher education. While this design allows for thematic flexibility and conceptual depth, several methodological limitations must be acknowledged. First, there is a risk of publication bias, as studies reporting positive attitudes or successful implementations may be more likely to be published and indexed, while critical or null findings remain underreported (Boell and Cecez-Kecmanovic, 2015). This can skew the thematic balance and over represent adoption-oriented perspectives.
Second, the rapid evolution of generative AI tools poses a challenge for literature reviews. Tools like ChatGPT, Copilot, or Bard are being updated continuously, meaning that the perceptions captured in current research may soon become outdated or incomplete (Michel-Villarreal et al., 2023). As new capabilities and ethical concerns emerge, longitudinal and iterative research designs will be needed to track these shifts over time. Finally, the exclusion of gray literature and non-English sources may have limited the scope of this review. Reports, policy briefs, and institutional case studies, often found outside academic databases could provide valuable insights into real-world implementation processes, particularly in underrepresented regions. Future reviews should consider broader inclusion criteria and adopt dynamic frameworks that respond to the evolving nature of AI in education.
5 Conclusion
This narrative review synthesized recent empirical literature to examine how artificial intelligence is reshaping academic work in higher education, with a focus on faculty perceptions, adoption barriers, ethical concerns, and evolving teaching roles. The findings reveal persistent ambivalence among instructors: while many recognize the potential of intelligent tools to enhance pedagogical efficiency, concerns remain regarding ethical use, professional displacement, and the erosion of academic autonomy. Adoption appears to be shaped by more than just technical familiarity. Organizational culture, the presence of clear institutional policies, and disciplinary traditions strongly influence faculty engagement with AI. Moreover, the absence of robust training opportunities and ethical guidance continues to limit meaningful integration into academic practices.
By framing the findings through models such as TAM and UTAUT, this review moves beyond description to offer explanatory insight into the mechanisms driving resistance or acceptance. It also underscores the need to foster AI literacy through multidimensional strategies that include pedagogical, ethical, and institutional dimensions. There is a strong emphasis on adapting faculty development and policy frameworks to specific regional contexts, particularly in underrepresented areas such as Latin America. Institutions are also encouraged to take a proactive role in fostering responsible, equitable, and critically informed uses of AI in education.
Author contributions
JB: Conceptualization, Investigation, Methodology, Visualization, Writing – original draft, Writing – review and editing. LL-A: Conceptualization, Investigation, Supervision, Validation, Writing – original draft, Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by Universidad Tecnológica Indoamérica, under the project “Innovación en la Educación Superior a través de las Tecnologías Emergentes,” Grant Number: IIDI-022-25.
Acknowledgments
We extend our gratitude to the EDUTEM research network for its support in the dissemination of results.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that Generative AI was used in the creation of this manuscript. The author(s) verify and take full responsibility for the use of generative AI in the preparation of this manuscript. Generative AI was used solely to revise English grammar and syntax.
Publisher’s note
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{
"date": "2024/12/01",
"position": 88,
"query": "artificial intelligence education"
}
] |
Alpha School: AI Powered Private School
|
AI Powered Private School
|
https://alpha.school
|
[] |
Academics at the right level and pace. Our AI tutor gives students 1:1 personalized education, providing coursework at their individual pace and the appropriate ...
|
Our 2hr Learning model harnesses adaptive technology to provide students 1:1 learning, accelerating mastery over core subjects.
With academics completed in the mornings, they get their afternoons back to gain real-world skills and explore interests, all at school.
The truth is clear: Your kids can accomplish twice as much if they’re not sitting in a one-size-fits-all classroom the whole day. / While old-school kids zone out for 6 hours, Alpha kids learn faster and love it.
| 2022-12-01T00:00:00 |
https://alpha.school/
|
[
{
"date": "2022/12/01",
"position": 91,
"query": "artificial intelligence education"
},
{
"date": "2024/09/01",
"position": 96,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 87,
"query": "artificial intelligence education"
},
{
"date": "2025/04/01",
"position": 56,
"query": "AI education"
},
{
"date": "2025/05/01",
"position": 67,
"query": "AI education"
},
{
"date": "2025/06/01",
"position": 96,
"query": "artificial intelligence education"
}
] |
|
Most Schools Don't Teach AI Ethics to All Students - Child Trends
|
Most Schools Don’t Teach AI Ethics to All Students
|
https://www.childtrends.org
|
[] |
Figure: Public school leaders' reporting on students being taught about the ethical/appropriate use of AI. Source: U.S. Department of Education, ...
|
Source: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, School Pulse Panel 2024–25.
Teaching AI ethics should not be optional
As AI becomes a regular part of how students learn and complete schoolwork, ethical instruction is more important than ever. Students are using AI tools, often without clear guidance, to write essays, solve math problems, and gather information. Yet most schools have not equipped students with the skills to evaluate AI’s accuracy, recognize bias, or understand when and how it should be used.
At Child Trends, we help school systems navigate this new landscape with research-based guidance and practical tools. Our AI-Class Framework helps schools integrate AI responsibly into instruction, while our AI Risk Framework offers actionable strategies to manage risks, protect students, and encourage ethical use.
Ethics must be foundational to AI education. Without it, students may use powerful tools without understanding their consequences. By prioritizing ethical instruction, schools can give students the knowledge they need to make responsible decisions and thrive in a technology-driven world.
| 2022-12-01T00:00:00 |
https://www.childtrends.org/publications/schools-students-ethics-ai
|
[
{
"date": "2022/12/01",
"position": 95,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 86,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 88,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 91,
"query": "artificial intelligence education"
}
] |
|
Survey: 60% of Teachers Used AI This Year and Saved up to 6 ...
|
Survey: 60% of Teachers Used AI This Year and Saved up to 6 Hours of Work a Week
|
https://www.fasa.net
|
[
"Lauren Wagner"
] |
... AI models,” the report said. “Everyone in education has a responsibility to harness the good to serve educational priorities while also ...
|
By Lauren Wagner
Nearly two-thirds of teachers utilized artificial intelligence this past school year, and weekly users saved almost six hours of work per week, according to a recently released Gallup survey. But 28% of teachers still oppose AI tools in the classroom.
The poll, published by the research firm and the Walton Family Foundation, includes perspectives from 2,232 U.S. public school teachers.
“[The results] reflect a keen understanding on the part of teachers that this is a technology that is here, and it’s here to stay,” said Zach Hrynowski, a Gallup research director. “It’s never going to mean that students are always going to be taught by artificial intelligence and teachers are going to take a backseat. But I do like that they’re testing the waters and seeing how they can start integrating it and augmenting their teaching activities rather than replacing them.”
At least once a month, 37% of educators take advantage of tools to prepare to teach, including creating worksheets, modifying materials to meet student needs, doing administrative work and making assessments, the survey found. Less common uses include grading, providing one-on-one instruction and analyzing student data.
A 2023 study from the RAND Corp. found the most common AI tools used by teachers include virtual learning platforms, like Google Classroom, and adaptive learning systems, like i-Ready or the Khan Academy. Educators also used chatbots, automated grading tools and lesson plan generators.
Most teachers who use AI tools say they help improve the quality of their work, according to the Gallup survey. About 61% said they receive better insights about student learning or achievement data, while 57% said the tools help improve their grading and student feedback.
Nearly 60% of teachers agreed that AI improves the accessibility of learning materials for students with disabilities. For example, some kids use text-to-speech devices or translators.
More teachers in the Gallup survey agreed on AI’s risks for students versus its opportunities. Roughly a third said students using AI tools weekly would increase their grades, motivation, preparation for jobs in the future and engagement in class. But 57% said it would decrease students’ independent thinking, and 52% said it would decrease critical thinking. Nearly half said it would decrease student persistence in solving problems, ability to build meaningful relationships and resilience for overcoming challenges.
In 2023, the U.S. Department of Education published a report recommending the creation of standards to govern the use of AI.
| 2022-12-01T00:00:00 |
https://www.fasa.net/Leader2Leader/survey-60-of-teachers-used-ai-this-year-and-saved-up-to-6-hours-of-work-a-week
|
[
{
"date": "2022/12/01",
"position": 96,
"query": "artificial intelligence education"
},
{
"date": "2023/11/01",
"position": 91,
"query": "artificial intelligence education"
},
{
"date": "2023/12/01",
"position": 92,
"query": "artificial intelligence education"
},
{
"date": "2024/12/01",
"position": 95,
"query": "artificial intelligence education"
}
] |
|
AFT to Launch National Academy for AI Instruction with Microsoft ...
|
AFT to Launch National Academy for AI Instruction with Microsoft, OpenAI, Anthropic and United Federation of Teachers
|
https://www.aft.org
|
[] |
The groundbreaking $23 million education initiative will provide access to free AI training and curriculum for all 1.8 million members of the ...
|
Press Release
NEW YORK – The AFT, alongside the United Federation of Teachers and lead partner Microsoft Corp., founding partner OpenAI, and Anthropic, announced the launch of the National Academy for AI Instruction today. The groundbreaking $23 million education initiative will provide access to free AI training and curriculum for all 1.8 million members of the AFT, starting with K-12 educators. It will be based at a state-of-the-art bricks-and-mortar Manhattan facility designed to transform how artificial intelligence is taught and integrated into classrooms across the United States.
The academy will help address the gap in structured, accessible AI training and provide a national model for AI-integrated curriculum and teaching that puts educators in the driver’s seat.
Teachers are facing tremendous technological changes, which include the challenges of navigating AI wisely, ethically and safely. They are overwhelmed and looking for ways to gain the skills they need to help their students succeed. The program is the first partnership between a national union and tech companies, structured to create a sustainable education infrastructure for AI.
“To best serve students, we must ensure teachers have a strong voice in the development and use of AI,” said Brad Smith, vice chair and president of Microsoft. “This partnership will not only help teachers learn how to better use AI, it will give them the opportunity to tell tech companies how we can create AI that better serves kids.”
The announcement was made at the headquarters of the AFT's largest affiliate, the 200,000-member New York City-based UFT, where hundreds of educators were on hand for a three-day training session, including six hours of AI-focused material that highlighted practical, hands-on ways to marry the emerging technology with established pedagogy.
“AI holds tremendous promise but huge challenges—and it’s our job as educators to make sure AI serves our students and society, not the other way around,” said AFT President Randi Weingarten. “The direct connection between a teacher and their kids can never be replaced by new technologies, but if we learn how to harness it, set commonsense guardrails and put teachers in the driver’s seat, teaching and learning can be enhanced.”
“The academy is a place where educators and school staff will learn about AI—not just how it works, but how to use it wisely, safely and ethically. This idea started with the partnership between lead partner Microsoft and the AFL-CIO in late 2023. We jointly hosted symposiums over the last two summers, but never reached critical mass to ensure America’s educators are coaches in the game, not spectators on the sidelines. Today’s announcement would not be possible without the cooperation of Microsoft, OpenAI, Anthropic and the leadership at the United Federation of Teachers, and I thank them for their efforts.”
"When it comes to AI in schools, the question is whether it is being used to disrupt education for the benefit of students and teachers or at their expense. We want this technology to be used by teachers for their benefit, by helping them to learn, to think and to create,” said Chris Lehane, chief global affairs officer of OpenAI. “This AI academy will help ensure that AI is being deployed to help educators do what they do best—teach—and in so doing, help advance the small-'d' democratizing power of education.”
"We're at a pivotal moment in education, and how we introduce AI to educators today will shape teaching for generations to come,” said Anthropic Co-founder and Head of Policy Jack Clark. “That's why we're thrilled to partner with the AFT to empower teachers with the knowledge and tools to guide their students through this evolving landscape. Together, we're building a future where AI supports great teaching in ethical and effective ways."
Anchored by the New York City facility, the National Academy for AI Instruction will serve as a premier hub for AI education , equipped with cutting-edge technology and operated under the leadership of the AFT and a coalition of public and private stakeholders. The academy will begin instruction later this fall and then scale nationally. Over five years, the program aims to support 400,000 educators—approximately 10 percent of the U.S. teaching workforce—reaching more than 7.2 million students.
Through the training of thousands of teachers annually and by offering credential pathways and continuing education credits, the academy will facilitate broad AI instruction and expand opportunity for all.
“For so long, there have been many new programs that were weaponized against educators,” said UFT President Michael Mulgrew. “Our goal is to develop a tool that gives educators the ability to train their AI and incorporate it into their instructional planning, giving them more one-on-one time with their students.”
“Sometimes as a teacher you suffer burnout and you can’t always communicate to the class in the right voice or find the right message, and I feel like these AI tools we are working with can really help with that—especially phrasing things in a way that helps students learn better,” says Marlee Katz, teacher for the deaf and hard of hearing in multiple New York City public schools in the borough of Queens. “The tools don’t take away your voice, but if I need to sound more professional or friendly or informed, I feel like these tools are like a best friend that can help you communicate. I love it.”
“As an instructional technology specialist for over 27 years, watching educators learn and work with AI reminds me of when teachers were first using word processors. We are watching educators transform the way people use technology for work in real time, but with AI it’s on another unbelievable level because it’s just so much more powerful,” says Vincent Pilato, New York City Public Schools K-8 educator and UFT Teacher Center director. “I think the UFT and the AFT were right to say AI is something educators should take ownership of, not only because it can assist with enhancing the way they interact with and meet the needs of students, but also because AI assists with educator workflow. It can be a thought partner when they're working by themselves, whether that’s late-night lesson planning, looking at student data or filing any types of reports—a tool that's going to be transformative for teachers and students alike.”
Together, Microsoft, OpenAI, Anthropic and the AFT are proud to help our nation’s teachers become AI-proficient educators and to leverage this unique partnership to democratize access to AI skills, ensuring that students from all backgrounds are prepared to thrive in an AI-driven future.
Designed by leading AI experts and experienced educators, the program will include workshops, online courses and hands-on training sessions, ensuring that teachers are well-equipped to navigate an AI-driven future. It will bring together interdisciplinary research teams to drive innovation in AI education and establish a national model for AI-integrated teaching environments. Finally, the academy will provide ongoing support and resources to help educators stay updated with the latest advancements in AI. Innovation labs and feedback cycles will ensure these tools are refined based on actual classroom experiences.
Through scalable training modules, virtual learning environments and credential pathways, the program empowers a diverse range of educators to become confident leaders in AI instruction. In turn, these teachers will bring AI literacy, ethical reasoning and creative problem-solving into classrooms that might otherwise be left behind in the digital transformation.
The idea for the academy was first proposed by venture capitalist, educator, activist and AFT member Roy Bahat. He is currently the head of Bloomberg Beta, the venture capital arm of Bloomberg, and will be joining the academy’s board of directors.
For more information about the National Academy for AI Instruction, please visit AIinstruction.org.
.
About the AFT
The AFT represents 1.8 million pre-K through 12th-grade teachers; paraprofessionals and other school-related personnel; higher education faculty and professional staff; federal, state and local government employees; nurses and healthcare workers; and early childhood educators.
About Microsoft
Microsoft (Nasdaq “MSFT” @microsoft) creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.
About OpenAI
OpenAI is an AI research and deployment company with a mission to ensure that artificial general intelligence benefits all of humanity.
About Anthropic
Anthropic is an AI safety and research company that creates reliable, interpretable, and steerable AI systems. Anthropic’s flagship product is Claude, a large language model trusted by millions of users worldwide. Learn more about Anthropic and Claude at anthropic.com.
About UFT
The UFT represents nearly 200,000 members and is the sole bargaining agent for most of the nonsupervisory educators who work in the New York City public schools. This includes teachers; retired members; classroom paraprofessionals; and many other school-based titles including school secretaries, school counselors, occupational and physical therapists, family child care providers, nurses, and other employees at several private educational institutions and some charter schools.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.aft.org/press-release/aft-launch-national-academy-ai-instruction-microsoft-openai-anthropic-and-united
|
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Artificial Intelligence (AI) in Healthcare & Medical Field
|
Artificial Intelligence (AI) in Healthcare & Medical Field
|
https://www.foreseemed.com
|
[] |
AI in healthcare is expected to play a major role in redefining the way we process healthcare data, diagnose diseases, develop treatments and even prevent them ...
|
The emergence of artificial intelligence (AI) in healthcare has been groundbreaking, reshaping the way we diagnose, treat and monitor patients. This technology is drastically improving healthcare research and outcomes by producing more accurate diagnoses and enabling more personalized treatments. AI in healthcare’s ability to analyze vast amounts of clinical documentation quickly helps medical professionals identify disease markers and trends that would otherwise be overlooked. The potential applications of AI and healthcare are broad and far-reaching, from scanning radiological images for early detection to predicting outcomes from electronic health records. By leveraging artificial intelligence in hospital settings and clinics, healthcare systems can become smarter, faster, and more efficient in providing care to millions of people worldwide. Artificial intelligence in healthcare is truly turning out to be the future – transforming how patients receive quality care while mitigating costs for providers and improving health outcomes.
It all began with IBM's Watson artificial intelligence system, which was developed to answer questions accurately and quickly. Articles on artificial intelligence in healthcare mention IBM’s launch of a healthcare-specific version of Watson in 2011 that focused on natural language processing—the technology used to understand and interpret human communication. Today, alongside IBM, other tech giants like Apple, Microsoft and Amazon are increasingly investing in AI technologies for the healthcare sector.
The potential implications of artificial intelligence in healthcare are truly remarkable. AI in healthcare is expected to play a major role in redefining the way we process healthcare data, diagnose diseases, develop treatments and even prevent them altogether. By using artificial intelligence in healthcare, medical professionals can make more informed decisions based on more accurate information - saving time, reducing costs and improving medical records management overall. From identifying new cancer treatments to improving patient experiences, AI in healthcare promises to be a game changer - leading the way towards a future where patients receive quality care and treatment faster and more accurately than ever before.
Let’s take a look at a few of the different types of artificial intelligence and healthcare industry benefits that can be derived from their use.
| 2022-12-01T00:00:00 |
https://www.foreseemed.com/artificial-intelligence-in-healthcare
|
[
{
"date": "2022/12/01",
"position": 2,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 2,
"query": "AI healthcare"
},
{
"date": "2023/01/01",
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"query": "artificial intelligence healthcare"
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{
"date": "2023/02/01",
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{
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{
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"query": "artificial intelligence healthcare"
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{
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/06/01",
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"query": "AI healthcare"
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{
"date": "2023/07/01",
"position": 2,
"query": "AI healthcare"
},
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"query": "artificial intelligence healthcare"
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{
"date": "2024/03/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
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{
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"date": "2024/10/01",
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"date": "2024/11/01",
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"query": "AI healthcare"
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"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
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},
{
"date": "2025/02/01",
"position": 2,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 2,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 2,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/05/01",
"position": 2,
"query": "artificial intelligence healthcare"
},
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"position": 2,
"query": "AI healthcare"
},
{
"date": "2025/06/01",
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"query": "artificial intelligence healthcare"
}
] |
|
Artificial intelligence: opportunities and implications for the health ...
|
Artificial intelligence: opportunities and implications for the health workforce
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Indrajit Hazarika",
"Policy Advisor",
"Division Of Health Systems",
"Western Pacific Regional Office",
"World Health Organization",
"United Nations Avenue",
"Ermita",
"Manila",
"Metro Manila"
] |
This article reviews the available literature to identify AI opportunities that can potentially transform the role of healthcare providers.
|
Abstract Healthcare involves cyclic data processing to derive meaningful, actionable decisions. Rapid increases in clinical data have added to the occupational stress of healthcare workers, affecting their ability to provide quality and effective services. Health systems have to radically rethink strategies to ensure that staff are satisfied and actively supported in their jobs. Artificial intelligence (AI) has the potential to augment provider performance. This article reviews the available literature to identify AI opportunities that can potentially transform the role of healthcare providers. To leverage AI’s full potential, policymakers, industry, healthcare providers and patients have to address a new set of challenges. Optimizing the benefits of AI will require a balanced approach that enhances accountability and transparency while facilitating innovation. Keywords: artificial intelligence, healthcare, health workers, productivity, professional liability, provider–patient relationship
Introduction Globally, the needs-based shortage of healthcare workers was estimated to be about 18 million in 2013.1 This shortage is being driven by a broad set of factors related to chronic underinvestment in health workforce education, recruitment and labour market constraints.2 The shortage and distribution challenges have led to increases in the workload and stress-related burnout of remaining staff, creating unsatisfactory work environments. This is not only driving up the rates of attrition, but is also causing a growing reluctance among new graduates to pursue a healthcare career. Recent studies3,4 have shown that the ongoing staffing shortages will not only increase patient waiting times, but also dramatically impact the overall quality of healthcare.5 Healthcare is a cyclic process wherein healthcare providers collect, analyse and integrate data from multiple sources and corroborate these data with empirical research and clinical expertise to derive actionable decisions.6 In the current environment, healthcare providers are exposed to a variety of occupational stressors, such as high workload, time pressure, administrative chores, low social support at work, uncertainty concerning patient treatment and predisposition towards emotional responses. They often struggle to cope with these changes. Among others, burnout of healthcare providers, especially clinical staff, has become an occupational hazard, with its rate reaching between 25% and 75% in some clinical specialties.7 Health systems have to radically rethink strategies to safeguard quality and safety, maximize efficiencies and ensure that staff are satisfied and actively supported in their jobs. Rapid, disruptive technological change has the potential to drive healthcare reforms to improve efficiency and augment provider satisfaction, thus improving patient experiences and outcomes.8 This prospect has focused global attention on the integration of technologies such as artificial intelligence (AI) to address current and emerging health system challenges, including the workforce.9,10
Current healthcare challenges: the role of technology Today’s healthcare environment is rapidly evolving. While some of these changes are evolutionary and incremental, others have been revolutionary and transformative. These changes have not only impacted clinical processes and practices of healthcare providers, but have also affected the experiences for patients and their families. A case in point is the exponential growth in biomedical and clinical evidence. The doubling time of medical knowledge in 1950 was estimated at 50 y, in 1980 it was 7 y and in 2010 it was 3.5 y. In 2020 it is projected to be only 73 d.11 The sheer volume of information, coupled with time constraints and cognitive limitations, has outstripped the capacity of healthcare providers to apply this new knowledge. Studies on the adoption of proven innovations in healthcare confirm that there is unjustifiable slowness and incomplete implementation of evidence-based practices, even in the best academic health centres.12 Clinical encounters are becoming increasingly complex, as patients present with multimorbidity. In the UK, for example, recent estimates suggest that one in six patients have more than one chronic condition, and these patients account for approximately one-third of all general practice consultations.13 Although multimorbidity is common and costly, clinical practice guidelines and models of care delivery are still largely built on vertical monomorbid approaches.14 The internet has revolutionized the way information is shared and accessed. Patients and their families now have more knowledge of, competence with and engagement in their health decision-making.15 They are also demanding more sophisticated, convenient, transparent, affordable and personalized care. Against the background of steady advances in clinical knowledge and changing patterns of health needs, the traditional model of decision-making by solo providers relying on their memory and personal experience is inadequate to effectively address twenty-first-century health challenges. In addition, the growth of consumerism and the proliferation of internet-accessible sources of health-related information will continue to modify the provider–patient relationship. Technological advancements have provided a breakthrough in addressing some of these challenges. From devices to medicines, technological developments have increased diagnosis and treatment options and contributed to improvements in efficiency and quality of healthcare. By transforming healthcare delivery, the use of technology has led to remarkable increases in longevity and quality of life.16 Until recently, technology has not been routinely used to gather, integrate and interpret data to formulate clinical decisions.17,18 Technologies such as AI have the potential to analyse and identify patterns in complex data and support healthcare providers in the delivery of care.19
The rise of AI Over the past decade, AI has moved from the realms of science fiction to a tangible technology that is increasingly becoming a part of everyday life. The healthcare space is no exception—AI is already being introduced into healthcare settings. AI involves the use of technologies such as natural language processing, deep learning, context aware processing and intelligent robotics. Analytics coupled with AI can potentially play an important role in the data mining of health records, thereby becoming an effective technique to guide healthcare decision-making. In contrast to analytics, which is based on a predefined set of programs, AI has the capability to self-learn using historical data. AI aims to mimic human cognitive functions.18 There are already multiple applications in healthcare, ranging from automated administrative tasks to clinical decision aids, automated imaging, intelligent drug design and AI-powered surgical robots. In advanced economies, the adoption of AI technology has gained interest and momentum, with the primary objectives being decreasing cost and improving healthcare outcomes. Although all countries are currently at an initial stage of adoption, in many emerging economies the growth has been slow due to the paucity of digitized healthcare data. Nevertheless, AI adoption is estimated to increase in the future. According to a recent report,20 AI in the healthcare market is expected to grow from US$2.1 billion in 2018 to US$36.1 billion by 2025, at a compound annual growth rate of 50.2% during the forecast period. The use of AI in healthcare is not new, but there have been rapid advances in the field in recent years. This has been enabled in part by notable progress in big data analytics and facilitated by growth in the availability of healthcare data. With appropriate analytical techniques, such as machine learning tools, AI can potentially revolutionize many aspects of healthcare.
AI to the rescue AI is likely to transform the role of healthcare providers, and may even dramatically change the provider–patient relationship. As automation gains momentum, there is enthusiasm about the prospects on the one hand, but there are also concerns that technology-fuelled increases in productivity will make certain healthcare jobs redundant.21 Although there is still much uncertainty about how adoption of AI will proceed, there are indications that AI has the potential to augment provider performance to deliver efficient, effective and quality care. Productivity AI is being deployed in various applications, including assisting in administrative tasks, mining health records, designing treatment plans and providing consultations. The use of AI can make certain time-consuming repetitive processes faster and more efficient. This allows healthcare providers to spend more time on tasks that focus on the clinical context of their patients and attending to their needs. In addition, the use of AI enables healthcare providers to manage care for a larger number of patients. In nursing, for instance, it has been reported that the use of AI-enabled tools increases productivity by 30–50%.22 The combination of AI and human intelligence, or ‘augmented intelligence’, has been touted as a powerful approach to deliver on the fundamental mission of healthcare.23 Workload High workload is a recognized occupational stressor that has implications for the quality of care and patient outcomes.24,25 Prior reviews26 have demonstrated that administrative tasks contribute substantially to staff workload and time pressure. For instance, it has previously been reported that, in ambulatory settings, physicians spend 49% of their time on electronic health records and desk work versus 33% on direct clinical face time with patients and staff.27 By auto-populating structured data fields from open-ended clinician notes, querying relevant data from prior clinical records and transcribing recorded patient encounters, AI has the potential to substantially reduce the administrative burden. A recent report estimates that voice-to-text transcription will result in work time savings of 17% for doctors and 51% for registered nurses.28 Technology giants such as Amazon are working on introducing a new machine learning service that will extract meaningful information from unstructured electronic health record (EHR) data and free-text clinical notes. Amazon Comprehend Medical makes it easier to analyse unstructured EHR data to extract key clinical terms related to a patient’s diagnoses, medications, symptoms, treatments and other interactions with the healthcare system.29 Performance AI tools have the potential to improve diagnostic decisions and treatment outcomes and reduce medical errors. AI has expanded substantially in the fields of medical imaging and diagnostics. Deep learning techniques are aiding in the prevention of errors in diagnostics and improving test outcomes. For instance, AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.30,31 Many healthcare providers are integrating AI into their daily functions to gather insights from the growing amount of clinical data, thereby minimizing patient risk. AI is also being used to automatically review clinical documents and either extract information for quality reporting or populate diagnosis coding.32 In some cases, AI tools have been combined with existing technologies to prevent healthcare providers from committing medical errors. For examples, start-ups like MedAware (Raanana, Israel) are integrating AI capabilities with EHR systems to prevent prescription errors.23 In addition, leveraging on the capabilities of machine learning applications, technology companies like Google (DeepMind), IBM (Watson) and others are currently conceptualizing the prospects of AI-powered surgical robots. It is expected that the use of robots with AI capabilities will increase precision, reduce damage and enhance the clinical pace of recovery. Teamwork The current care environment requires healthcare providers to collaborate and work in teams. This necessitates strong communication for shared decision-making, coordinated actions and evaluation of progress. AI has the potential to integrate data from a large number of sources, both structured and unstructured, to provide more cohesive, faster, more consistent access to patient information across different settings and disciplines. In some cases, chatbots have been used to coordinate and schedule medical appointments, provide reminders and notify providers regarding a patient’s condition based on symptoms. Start-ups like Babylon Health (London, UK) and Your.MD (London, UK) are AI-powered healthcare assistant applications that support providers, patients and caregivers, providing the above functionalities. Satisfaction Effective evidence-informed decisions require the gathering, integration and interpretation of vast amounts of data. Given the enormity and complexity of the data, healthcare providers are able to draw on a subset of the information. For instance, only about one-fifth of the available trial results are used when diagnosing and treating cancer patients.18 AI technologies have the potential to process data, discover new knowledge and create novel methods to improve the quality of healthcare. AI is also being used to monitor patient outcomes. For instance, the National Institutes of Health has created the AiCure app to monitor the use of medication by a patient.33 Thorough use of current research evidence and regular follow-up of patient outcomes can promote increased job satisfaction. AI can enable patients with chronic conditions to become better informed about their health and stay connected with health caregivers. For example, home health monitoring technology powered by AI could help the frail and elderly stay connected with professional caregivers to ensure they receive timely care when needed. Individuals with diabetes or hypertension could benefit from similar technology that allows them to track their condition via clinically validated sensors and devices. This allows providers to extend patient care efforts outside of office hours and drive self-management.
Newer challenges AI has vast potential to deliver better quality and more efficient healthcare, thereby enhancing productivity, provider satisfaction and user experience and dramatically improving outcomes. To leverage on AI’s full potential, policymakers, industry, healthcare providers and patients have to address a new set of challenges. Professional liability Traditionally, clinical decision-making is within the purview of qualified, licensed healthcare providers. As AI becomes increasingly used to assist with clinical activities, the professional obligations of healthcare providers towards individual patients might be affected by the use of AI decision support systems. Considering the potential for AI to make erroneous decisions, the legal liability of AI-supported decisions often remains ambiguous. This is further complicated by the fact that the development of appropriate legal principles and guidance is often slower than technology’s advancing capabilities. A related concern is that AI could make healthcare providers complacent and less likely to check results and challenge errors.34 Labour market implications As with many new technologies, the introduction of AI is likely to mean the skills and expertise required of healthcare providers will change. In some areas, AI could enable automation of tasks that have previously been carried out by humans. Moreover, as AI continues to evolve in healthcare, there will likely be a growth in the demand for new skill sets, such as informatics. Education and training programmes will have to be adjusted to match the labour market needs. There are also some concerns that the introduction of AI systems might be used to justify the employment of less-skilled staff. This could be problematic if the technology fails and staff are not able to recognize errors or carry out necessary tasks without computing assistance.34 Provider competencies Future medical practice will be characterized by the use of a growing array of data from multiple sources and AI applications. Newer competencies will be required to better use the findings of cognitive computing systems, and the integration of technology and patients and their families in the care journey. To be effective, healthcare providers must be able to effectively leverage data platforms, analyse outcomes, improve performance and communicate the meaning of the probabilities generated by massive amounts of data to patients and their families. Health profession education will have to move beyond the foundational biomedical and clinical sciences. Curricular reforms will have to incorporate competencies such as the use of intelligence tools involving large datasets, machine learning and robotics, while guaranteeing the mastery of people-centred care.35 Ethical considerations AI’s current strength is in its ability to learn from complex datasets and recognize patterns. Application of AI technology is limited by the quality of available healthcare data. AI might not work as well where data are scarce or more difficult to collect or render digitally. Concerns have been raised that datasets used to train AI systems have inherent biases and are often not representative of the wider population.34 Functioning largely as ‘black boxes’, the behaviour of AI systems is difficult to interpret and explain. The difficulties in validating the outputs of AI systems have raised concerns about accountability, transparency and human control.36 At a practical level, both patients and healthcare providers will need to be able to trust AI systems if they are to be implemented successfully in healthcare. Regulatory compliance As the use of AI in the clinical space increases and evolves, legal and regulatory risk will increase, particularly since traditional regulatory principles are not yet attuned to AI. The evidence standard for certain AI tasks is currently ill-defined. For example, validating the accuracy of AI-enabled imaging applications against current quality standards for traditional imaging is likely sufficient for clinical use. For AI applications that predict, diagnose and provide treatment information, the evidence standards have to be significantly higher. A key challenge for future governance of AI technologies will be to ensure that AI is developed and used in a way that is compatible with the public interest while stimulating and driving innovation in the sector. This implies a regulatory framework that is supportive of technological innovations but safeguards leakage of sensitive information to unauthorized third parties and protects the patient’s right to privacy.37 Provider–patient relationship A well-functioning provider–patient relationship is still the essence of healthcare. The success of providing care depends on collaboration, empathy and shared decision-making. Empathy skills of healthcare providers have been shown to positively influence patient outcomes.38 AI can assist in improving efficiency and quality, but is limited by its inability to possess some human characteristics, such as compassion, empathy and the human touch. Future developments in AI technology may redefine the relationship between providers, patients and their caregivers. For now, the role of healthcare providers is unchanged, but AI can be a very useful cognitive assistant.
Conclusions AI is becoming a part of our daily lives, and the healthcare ecosystem is no exception. As AI continues to advance, its functionalities will potentially transform the healthcare space. Integration of AI will assist with care provision, with the advantage of increasing efficiency and improving the quality of certain services, leading to a higher volume of care delivery. While AI has tremendous potential to address important health challenges, it might be limited by the availability and quality of health data and its inherent inability to display some human characteristics. Using pattern recognition, AI can assist healthcare providers with more informed clinical decision-making and enable patients to take an active role in their own health. It can automate repetitive tasks, allowing healthcare providers to focus on higher-level cognitive tasks and patient care. AI has enormous potential to enhance productivity, improve quality and efficiency and contribute towards higher provider and patient satisfaction. As impressive as AI is as a cognitive assistant, its growing use in healthcare has posed a set of new challenges. These include concerns about ethical and medico-legal impacts; labour market implications, including effects on the roles and competencies of healthcare providers, and concerns arising from inherent data biases and data protection. AI has a huge potential to alleviate some of the challenges that healthcare providers face. Optimizing the benefits of AI will require a balanced approach that enhances accountability and transparency while facilitating innovation, fostering responsible access to data to further develop computing abilities and building trust between providers, patients, researchers and innovators. Looking into the future—taking cues from the history of automation—AI is unlikely to displace humans, but will definitely redefine their roles and establish itself as an indispensable cognitive assistant.39
Acknowledgements None.
Authors’ contributions The author is a staff member of the World Health Organization. The author alone is responsible for the views expressed in this publication and they do not represent the decisions or policies of the World Health Organization.
Funding None.
Competing interests None declared.
| 2020-04-17T00:00:00 |
2020/04/17
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC7322190/
|
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How is AI being Used in the Healthcare Industry
|
How is AI being Used in the Healthcare Industry
|
https://www.lapu.edu
|
[] |
How is AI being Used in the Healthcare Industry.
|
From healthcare to finance and even transportation, artificial intelligence (AI) has become an integral part of society. But what is AI? AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning from experience, recognizing patterns, and making decisions based on data analysis. Its influence is only set to intensify alongside ongoing technological advancements, thus making it even more prominent in our everyday lives. With that being said, it’s no surprise that AI is becoming increasingly prevalent in the healthcare industry. In this blog, we’ll navigate the current landscape of AI in healthcare, delve into the anticipated future trends, review the importance of AI in healthcare, and dissect the advantages and potential challenges, in addition to integration into healthcare career programs .
Topic Overview:
AI in Healthcare Today
Where is AI Going in Healthcare?
Importance of AI in Healthcare
Advantages & Disadvantages of AI in Healthcare
AI in Healthcare Today
AI has been a hot topic and has captured a considerable amount of attention due to the recent advancements and implementation of this type of technology. When it comes to healthcare, AI is already actively being used in this industry on a smaller level, but there are various factors that prevent large-scale automation which we will further explore.
As of today, AI is primarily utilized to increase speed and accuracy in the healthcare realm. Some of the current uses of AI in this field include:
Diagnosing Patients: AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to assist healthcare professionals in accurate and swift diagnoses.
Transcribing Medical Documents: Automatic Speech Recognition (ASR) technology employs advanced algorithms and machine learning models to convert spoken language into written text, providing a more efficient and accurate method for documenting medical information.
Drug Discovery and Development: AI accelerates the drug discovery process by analyzing vast datasets to identify potential drug candidates and predict their efficacy.
Administrative Efficiency: AI streamlines administrative tasks, such as billing and scheduling, reducing paperwork and improving overall operational efficiency within healthcare organizations.
Each of these uses are done through different AI technologies, as “AI is not one technology, but rather a collection of them” ( NCBI ).
Examples of AI technologies:
Machine Learning – one of the most common forms of AI that is a broad technique at the core of many approaches to AI Neural Network – type of machine learning that is more complex than traditional machine learning Deep Learning – type of machine learning that is the most complex form
Natural Language Processing – enables computers to interpret and use human language and includes two basic approaches: Statistical Semantic
Rule-Based Expert Systems – is a collection of ‘if-then’ rules which require human experts and knowledge engineers
These examples showcase the versatility of AI technologies, each contributing to various applications and industries, reshaping the way we interact with and leverage technology in our daily lives.
Where is AI Going in Healthcare?
AI already plays an important role in healthcare, and it has a very bright future as capabilities continue to advance and grow. It is positioned to improve patient outcomes, increase safety, reduce human error, and reduce costs associated with healthcare, along with many other opportunities and changes in career programs . According to the National Library of Medicine, “we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10” ( NCBI ). Several key directions indicate where AI is heading in the healthcare sector. A few include enchanted diagnostics and personal treatment, analytics for disease prevention, drug discovery and development and human-AI collaboration.
Importance of AI in Healthcare
AI is important in healthcare for many reasons. The main reason is that “healthcare systems can become smarter, faster, and more efficient in providing care to millions of people worldwide” ( Foresee Medical ).This will provide patients with quality care while also reducing costs associated with healthcare. By leveraging AI, healthcare systems can optimize and expedite various processes, ranging from diagnostics and treatment planning to administrative tasks, resulting in improved patient outcomes.
Advantages & Disadvantages of AI in Healthcare
While AI in healthcare has many benefits, it also has potential challenges and disadvantages that may rise. AI presents a myriad of opportunities for the healthcare sector but this transformative journey is not without its challenges. Below are key advantages that propel the industry forward and the inherent disadvantages that demand careful navigation for a future where AI seamlessly integrates into the fabric of healthcare delivery.
Advantages of AI in Healthcare:
Improved diagnostics and precision medicine Streamlined administrative tasks and workflow Enhanced research and development
Disadvantages of AI in Healthcare:
Ethical concerns and data privacy issues Potential job displacement and human-AI collaboration challenges Reliability and trust issues in AI-driven decision-making
Conclusion:
While AI in healthcare has gained significant traction, the irreplaceable value of human skills, particularly empathy and compassion, are still needed and greatly valued in healthcare settings. The National Library of Medicine aptly emphasizes that AI systems are poised to complement rather than replace human clinicians on a large scale, augmenting their capacities to provide more effective and personalized patient care. As the integration of AI progresses, a diverse spectrum of responses is expected; some individuals will readily embrace the benefits it brings to healthcare, while others may exhibit resistance to working alongside and accepting artificial intelligence in their professional domain. The coexistence of human expertise and AI innovation will likely define the future landscape of healthcare, fostering a harmonious balance between technological advancement and compassionate care.
| 2023-12-21T00:00:00 |
2023/12/21
|
https://www.lapu.edu/post/ai-health-care-industry
|
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Harnessing artificial intelligence for health
|
Harnessing artificial intelligence for health
|
https://www.who.int
|
[] |
"AI is already playing a role in diagnosis and clinical care, drug development, disease surveillance, outbreak response, and health systems management …
|
Prioritizing AI for health is crucial, given its potential to enhance healthcare and address global health challenges, including the achievement of Sustainable Development Goals. The urgency is exacerbated by a significant pacing gap, with technology outpacing legal frameworks. WHO is actively guiding Member States, developing ethical standards, and convening expert groups to address these challenges, promoting responsible AI development, and fostering collaboration among stakeholders to mitigate risks and safeguard public health and trust.
WHO envisions a future where AI serves as a powerful force for innovation, equity, and ethical integrity in healthcare. The overall goal is to help Member States take AI to the people to enable enhanced, sustainable, and smarter health care.
| 2022-12-01T00:00:00 |
https://www.who.int/teams/digital-health-and-innovation/harnessing-artificial-intelligence-for-health
|
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|
Artificial intelligence in healthcare - Wikipedia
|
Artificial intelligence in healthcare
|
https://en.wikipedia.org
|
[] |
Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data.
|
Overview of the use of artificial intelligence in healthcare
X-ray of a hand, with automatic calculation of bone age by a computer software
Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data. In some cases, it can exceed or augment human capabilities by providing better or faster ways to diagnose, treat, or prevent disease.[1][2][3][4]
As the widespread use of artificial intelligence in healthcare is still relatively new, research is ongoing into its applications across various medical subdisciplines and related industries. AI programs are being applied to practices such as diagnostics,[5] treatment protocol development,[6] drug development,[7] personalized medicine,[8] and patient monitoring and care.[9] Since radiographs are the most commonly performed imaging tests in radiology, the potential for AI to assist with triage and interpretation of radiographs is particularly significant.[10]
Using AI also presents unprecedented ethical concerns related to issues such as data privacy, automation of jobs, and amplifying already existing biases.[11] Furthermore, new technologies such as AI are often resisted by healthcare leaders, leading to slow and erratic adoption.[12] In contrast, there are also several cases where AI has been put to use in healthcare without proper testing.[13][14][15][16] A systematic review and thematic analysis in 2023 showed that most stakeholders including health professionals, patients, and the general public doubted that care involving AI could be empathetic.[17] Moreover, meta-studies have found that the scientific literature on AI in healthcare often suffers from a lack of reproducibility.[18][19][20][21]
Applications in healthcare systems [ edit ]
Disease diagnosis [ edit ]
Accurate and early diagnosis of diseases is still a challenge in healthcare. Recognizing medical conditions and their symptoms is a complex problem. AI can assist clinicians with its data processing capabilities to save time and improve accuracy.[22] Through the use of machine learning, artificial intelligence can be able to substantially aid doctors in patient diagnosis through the analysis of mass electronic health records (EHRs).[23] AI can help early prediction, for example, of Alzheimer's disease and dementias, by looking through large numbers of similar cases and possible treatments.[24]
Doctors' decision making could also be supported by AI in urgent situations, for example in the emergency department. Here AI algorithms can help prioritize more serious cases and reduce waiting time. Decision support systems augmented with AI can offer real-time suggestions and faster data interpretation to aid the decisions made by healthcare professionals.[22]
In 2023 a study reported higher satisfaction rates with ChatGPT-generated responses compared with those from physicians for medical questions posted on Reddit's r/AskDocs.[25] Evaluators preferred ChatGPT's responses to physician responses in 78.6% of 585 evaluations, noting better quality and empathy. The authors noted that these were isolated questions taken from an online forum, not in the context of an established patient-physician relationship.[25] Moreover, responses were not graded on the accuracy of medical information, and some have argued that the experiment was not properly blinded, with the evaluators being coauthors of the study.[26][27][28]
Recent developments in statistical physics, machine learning, and inference algorithms are also being explored for their potential in improving medical diagnostic approaches.[29] Also, the establishment of large healthcare-related data warehouses of sometimes hundreds of millions of patients provides extensive training data for AI models.[30]
Electronic health records [ edit ]
Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, some anticipate the use of artificial intelligence to interpret the records and provide new information to physicians.[31]
One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms.[31] For example, the term heart attack and myocardial infarction mean the same things, but physicians may use one over the other based on personal preferences.[31] NLP algorithms consolidate these differences so that larger datasets can be analyzed.[31] Another use of NLP identifies phrases that are redundant due to repetition in a physician's notes and keeps the relevant information to make it easier to read.[31] Other applications use concept processing to analyze the information entered by the current patient's doctor to present similar cases and help the physician remember to include all relevant details.[32]
Beyond making content edits to an EHR, there are AI algorithms that evaluate an individual patient's record and predict a risk for a disease based on their previous information and family history.[33] One general algorithm is a rule-based system that makes decisions similarly to how humans use flow charts.[34] This system takes in large amounts of data and creates a set of rules that connect specific observations to concluded diagnoses.[34] Thus, the algorithm can take in a new patient's data and try to predict the likeliness that they will have a certain condition or disease.[34] Since the algorithms can evaluate a patient's information based on collective data, they can find any outstanding issues to bring to a physician's attention and save time.[33] One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response.[35] These methods are helpful due to the fact that the amount of online health records doubles every five years.[33] Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients.[33]
Drug interactions [ edit ]
Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature.[36][37][38][39] Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken.[40] To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.[41] Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were.[42] Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms.[36][37][39]
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.[37][38] Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization's VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.[43]
Telemedicine [ edit ]
An elderly man using a pulse oximeter to measure his blood oxygen levels
The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications.[44] AI can assist in caring for patients remotely by monitoring their information through sensors.[45] A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of.[45]
Another application of artificial intelligence is chat-bot therapy. Some researchers charge that the reliance on chatbots for mental healthcare does not offer the reciprocity and accountability of care that should exist in the relationship between the consumer of mental healthcare and the care provider (be it a chat-bot or psychologist), though.[46] Some examples of these chatbots include Woebot, Earkick and Wysa.[47][48][49]
Since the average age has risen due to a longer life expectancy, artificial intelligence could be useful in helping take care of older populations.[50] Tools such as environment and personal sensors can identify a person's regular activities and alert a caretaker if a behavior or a measured vital is abnormal.[50] Although the technology is useful, there are also discussions about limitations of monitoring in order to respect a person's privacy since there are technologies that are designed to map out home layouts and detect human interactions.[50]
Workload management [ edit ]
AI has the potential to streamline care coordination and reduce the workload. AI algorithms can automate administrative tasks, prioritize patient needs and facilitate seamless communication in a healthcare team.[51] This enables healthcare providers to focus more on direct patient care and ensures the efficient and coordinated delivery of healthcare services.
Clinical applications [ edit ]
Cardiovascular [ edit ]
Artificial intelligence algorithms have shown promising results in accurately diagnosing and risk stratifying patients with concern for coronary artery disease, showing potential as an initial triage tool.[52][53] Other algorithms have been used in predicting patient mortality, medication effects, and adverse events following treatment for acute coronary syndrome.[52] Wearables, smartphones, and internet-based technologies have also shown the ability to monitor patients' cardiac data points, expanding the amount of data and the various settings AI models can use and potentially enabling earlier detection of cardiac events occurring outside of the hospital.[54] A research in 2019 found that AI can be used to predict heart attack with up to 90% accuracy.[55] Another growing area of research is the utility of AI in classifying heart sounds and diagnosing valvular disease.[56] Challenges of AI in cardiovascular medicine have included the limited data available to train machine learning models, such as limited data on social determinants of health as they pertain to cardiovascular disease.[57]
A key limitation in early studies evaluating AI were omissions of data comparing algorithmic performance to humans. Examples of studies which assess AI performance relative to physicians includes how AI is non-inferior to humans in interpretation of cardiac echocardiograms[58] and that AI can diagnose heart attack better than human physicians in the emergency setting, reducing both low-value testing and missed diagnoses.[59]
In cardiovascular tissue engineering and organoid studies, AI is increasingly used to analyze microscopy images, and integrate electrophysiological read outs.[60]
Dermatology [ edit ]
Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology[61] and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. Machine learning learning holds great potential to process these images for better diagnoses.[62] Han et al. showed keratinocytic skin cancer detection from face photographs.[63] Esteva et al. demonstrated dermatologist-level classification of skin cancer from lesion images.[64] Noyan et al. demonstrated a convolutional neural network that achieved 94% accuracy at identifying skin cells from microscopic Tzanck smear images.[65] A concern raised with this work is that it has not engaged with disparities related to skin color or differential treatment of patients with non-white skin tones.[66]
According to some researchers, AI algorithms have been shown to be more effective than dermatologists at identifying cancer.[67] However, a 2021 review article found that a majority of papers analyzing the performance of AI algorithms designed for skin cancer classification failed to use external test sets.[68] Only four research studies were found in which the AI algorithms were tested on clinics, regions, or populations distinct from those it was trained on, and in each of those four studies, the performance of dermatologists was found to be on par with that of the algorithm. Moreover, only one study[69] was set in the context of a full clinical examination; others were based on interaction through web-apps or online questionnaires, with most based entirely on context-free images of lesions. In this study, it was found that dermatologists significantly outperformed the algorithms. Many articles claiming superior performance of AI algorithms also fail to distinguish between trainees and board-certified dermatologists in their analyses.[68]
It has also been suggested that AI could be used to automatically evaluate the outcome of maxillo-facial surgery or cleft palate therapy in regard to facial attractiveness or age appearance.[70][71]
Gastroenterology [ edit ]
AI can play a role in various facets of the field of gastroenterology. Endoscopic exams such as esophagogastroduodenoscopies (EGD) and colonoscopies rely on rapid detection of abnormal tissue. By enhancing these endoscopic procedures with AI, clinicians can more rapidly identify diseases, determine their severity, and visualize blind spots. Early trials in using AI detection systems of early stomach cancer have shown sensitivity close to expert endoscopists.[72]
AI can assist doctors treating ulcerative colitis in detecting the microscopic activity of the disease in people and predicting when flare-ups will happen. For example, an AI-powered tool was developed to analyse digitised bowel samples (biopsies). The tool was able to distinguish with 80% accuracy between samples that show remission of colitis and those with active disease. It also predicted the risk of a flare-up happening with the same accuracy. These rates of successfully using microscopic disease activity to predict disease flare are similar to the accuracy of pathologists.[73][74]
Obstetrics and gynaecology [ edit ]
Artificial intelligence utilises massive amounts of data to help with predicting illness, prevention, and diagnosis, as well as patient monitoring. In obstetrics, artificial intelligence is utilized in magnetic resonance imaging, ultrasound, and foetal cardiotocography. AI contributes in the resolution of a variety of obstetrical diagnostic issues.[75]
Infectious diseases [ edit ]
AI has shown potential in both the laboratory and clinical spheres of infectious disease medicine.[76] During the COVID-19 pandemic, AI has been used for early detection, tracking virus spread and analysing virus behaviour, among other things.[77] However, there were only a few examples of AI being used directly in clinical practice during the pandemic itself.[78]
Other applications of AI around infectious diseases include support-vector machines identifying antimicrobial resistance, machine learning analysis of blood smears to detect malaria, and improved point-of-care testing of Lyme disease based on antigen detection. Additionally, AI has been investigated for improving diagnosis of meningitis, sepsis, and tuberculosis, as well as predicting treatment complications in hepatitis B and hepatitis C patients.[76]
Musculoskeletal [ edit ]
AI has been used to identify causes of knee pain that doctors miss, that disproportionately affect Black patients.[79] Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Researchers have conducted a study using a machine-learning algorithm to show that standard radiographic measures of severity overlook objective but undiagnosed features that disproportionately affect diagnosis and management of underserved populations with knee pain. They proposed that new algorithmic measure ALG-P could potentially enable expanded access to treatments for underserved patients.[80]
Neurology [ edit ]
The use of AI technologies has been explored for use in the diagnosis and prognosis of Alzheimer's disease (AD). For diagnostic purposes, machine learning models have been developed that rely on structural MRI inputs.[81] The input datasets for these models are drawn from databases such as the Alzheimer's Disease Neuroimaging Initiative.[82] Researchers have developed models that rely on convolutional neural networks with the aim of improving early diagnostic accuracy.[83] Generative adversarial networks are a form of deep learning that have also performed well in diagnosing AD.[84] There have also been efforts to develop machine learning models into forecasting tools that can predict the prognosis of patients with AD. Forecasting patient outcomes through generative models has been proposed by researchers as a means of synthesizing training and validation sets.[85] They suggest that generated patient forecasts could be used to provide future models larger training datasets than current open access databases.
Oncology [ edit ]
AI has been explored for use in cancer diagnosis, risk stratification, molecular characterization of tumors, and cancer drug discovery. A particular challenge in oncologic care that AI is being developed to address is the ability to accurately predict which treatment protocols will be best suited for each patient based on their individual genetic, molecular, and tumor-based characteristics.[86] AI has been trialed in cancer diagnostics with the reading of imaging studies and pathology slides.[87]
In January 2020, Google DeepMind announced an algorithm capable of surpassing human experts in breast cancer detection in screening scans.[88][89] A number of researchers, including Trevor Hastie, Joelle Pineau, and Robert Tibshirani among others, published a reply claiming that DeepMind's research publication in Nature lacked key details on methodology and code, "effectively undermin[ing] its scientific value" and making it impossible for the scientific community to confirm the work.[90] In the MIT Technology Review, author Benjamin Haibe-Kains characterized DeepMind's work as "an advertisement" having little to do with science.[91]
In July 2020, it was reported that an AI algorithm developed by the University of Pittsburgh achieves the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity.[92][93] In 2023 a study reported the use of AI for CT-based radiomics classification at grading the aggressiveness of retroperitoneal sarcoma with 82% accuracy compared with 44% for lab analysis of biopsies.[94][95]
Ophthalmology [ edit ]
Artificial intelligence-enhanced technology is being used as an aid in the screening of eye disease and prevention of blindness.[96] In 2018, the U.S. Food and Drug Administration authorized the marketing of the first medical device to diagnose a specific type of eye disease, diabetic retinopathy using an artificial intelligence algorithm.[97] Moreover, AI technology may be used to further improve "diagnosis rates" because of the potential to decrease detection time.[98]
Pathology [ edit ]
Ki67 stain calculation by the open-source software QuPath in a pure seminoma, which gives a measure of the proliferation rate of the tumor. The colors represent the intensity of expression: blue-no expression, yellow-low, orange-moderate, and red-high expression. [ 99 ]
For many diseases, pathological analysis of cells and tissues is considered to be the gold standard of disease diagnosis. Methods of digital pathology allows microscopy slides to be scanned and digitally analyzed. AI-assisted pathology tools have been developed to assist with the diagnosis of a number of diseases, including breast cancer, hepatitis B, gastric cancer, and colorectal cancer. AI has also been used to predict genetic mutations and prognosticate disease outcomes.[72] AI is well-suited for use in low-complexity pathological analysis of large-scale screening samples, such as colorectal or breast cancer screening, thus lessening the burden on pathologists and allowing for faster turnaround of sample analysis.[100] Several deep learning and artificial neural network models have shown accuracy similar to that of human pathologists,[100] and a study of deep learning assistance in diagnosing metastatic breast cancer in lymph nodes showed that the accuracy of humans with the assistance of a deep learning program was higher than either the humans alone or the AI program alone.[101] Additionally, implementation of digital pathology is predicted to save over $12 million for a university center over the course of five years,[102] though savings attributed to AI specifically have not yet been widely researched. The use of augmented and virtual reality could prove to be a stepping stone to wider implementation of AI-assisted pathology, as they can highlight areas of concern on a pathology sample and present them in real-time to a pathologist for more efficient review.[100] AI also has the potential to identify histological findings at levels beyond what the human eye can see,[100] and has shown the ability to use genotypic and phenotypic data to more accurately detect the tumor of origin for metastatic cancer.[103] One of the major current barriers to widespread implementation of AI-assisted pathology tools is the lack of prospective, randomized, multi-center controlled trials in determining the true clinical utility of AI for pathologists and patients, highlighting a current area of need in AI and healthcare research.[100]
Primary care [ edit ]
Primary care has become one key development area for AI technologies.[104][105] AI in primary care has been used for supporting decision making, predictive modeling, and business analytics.[106] There are only a few examples of AI decision support systems that were prospectively assessed on clinical efficacy when used in practice by physicians. But there are cases where the use of these systems yielded a positive effect on treatment choice by physicians.[107]
As of 2022 in relation to elder care, AI robots had been helpful in guiding older residents living in assisted living with entertainment and company. These bots are allowing staff in the home to have more one-on-one time with each resident, but the bots are also programmed with more ability in what they are able to do; such as knowing different languages and different types of care depending on the patient's conditions. The bot is an AI machine, which means it goes through the same training as any other machine - using algorithms to parse the given data, learn from it and predict the outcome in relation to what situation is at hand.[108]
Psychiatry [ edit ]
In psychiatry, AI applications are still in a phase of proof-of-concept.[109] Areas where the evidence is widening quickly include predictive modelling of diagnosis and treatment outcomes,[110] chatbots, conversational agents that imitate human behaviour and which have been studied for anxiety and depression.[111]
Challenges include the fact that many applications in the field are developed and proposed by private corporations, such as the screening for suicidal ideation implemented by Facebook in 2017.[112] Such applications outside the healthcare system raise various professional, ethical and regulatory questions.[113] Another issue is often with the validity and interpretability of the models. Small training datasets contain bias that is inherited by the models, and compromises the generalizability and stability of these models. Such models may also have the potential to be discriminatory against minority groups that are underrepresented in samples.[114]
In 2023, US-based National Eating Disorders Association replaced its human helpline staff with a chatbot but had to take it offline after users reported receiving harmful advice from it.[115][116][117]
Radiology [ edit ]
AI is being studied within the field of radiology to detect and diagnose diseases through computerized tomography (CT) and magnetic resonance (MR) imaging.[118] It may be particularly useful in settings where demand for human expertise exceeds supply, or where data is too complex to be efficiently interpreted by human readers.[119] Several deep learning models have shown the capability to be roughly as accurate as healthcare professionals in identifying diseases through medical imaging, though few of the studies reporting these findings have been externally validated.[120] AI can also provide non-interpretive benefit to radiologists, such as reducing noise in images, creating high-quality images from lower doses of radiation, enhancing MR image quality,[121] and automatically assessing image quality.[122] Further research investigating the use of AI in nuclear medicine focuses on image reconstruction, anatomical landmarking, and the enablement of lower doses in imaging studies.[123] The analysis of images for supervised AI applications in radiology encompasses two primary techniques at present: (1) convolutional neural network-based analysis; and (2) utilization of radiomics.[119]
AI is also used in breast imaging for analyzing screening mammograms and can participate in improving breast cancer detection rate[124] as well as reducing radiologist's reading workload.
Pharmacy [ edit ]
In pharmacy, AI helps discover, develop and deliver medications, and can enhance patient care through personalized treatment plans.[125][126]
Industry [ edit ]
The trend of large health companies merging has allowed for greater health data accessibility. Greater health data have layed the groundwork to implement AI algorithms.
A large part of industry focus has been in the clinical decision support systems. As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions.[118] Numerous companies have been exploring the possibilities of the incorporation of big data in the healthcare industry, many of whom have been investigating market opportunities through "data assessment, storage, management, and analysis technologies".[127] With the market for AI expanding, large tech companies such as Apple, Google, Amazon, and Baidu all have their own AI research divisions, as well as millions of dollars allocated for acquisition of smaller AI based companies.[127]
Large companies [ edit ]
The following are examples of large companies that are contributing to AI algorithms for use in healthcare:
Smaller companies, applications [ edit ]
Neuralink has come up with a next-generation neuroprosthetic which intricately interfaces with thousands of neural pathways in the brain.[118] Their process allows a chip, roughly the size of a quarter, to be inserted in the place of a chunk of a skull by a precision surgical robot to avoid accidental injury.[118]
Elon Musk premiering the surgical robot that implants the Neuralink brain chip
Tencent has been working on several medical systems and services. These include AI Medical Innovation System (AIMIS), an AI-powered diagnostic medical imaging service; WeChat Intelligent Healthcare; and Tencent Doctorwork[citation needed]
Digital consultant apps use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to identify potential illnesses. These apps vary depending on the type of value generated for the target user (e.g. patient focus vs. healthcare provider and payer focus) and the value capturing mechanisms (e.g. providing information or connecting stakeholders).[citation needed]
The Indian startup Haptik developed a WhatsApp chatbot in 2021 which answered questions associated with coronavirus in India. Similarly, a software platform ChatBot in partnership with medtech startup Infermedica launched COVID-19 Risk Assessment ChatBot.[132]
Expanding care to developing nations [ edit ]
Artificial intelligence continues to expand in its abilities to diagnose more people accurately in nations where fewer doctors are accessible to the public. Many new technology companies such as SpaceX and the Raspberry Pi Foundation have enabled more developing countries to have access to computers and the internet than ever before.[133] With the increasing capabilities of AI over the internet, advanced machine learning algorithms can allow patients to get accurately diagnosed when they would previously have no way of knowing if they had a life-threatening disease or not.[133]
Using AI in developing nations that do not have the resources will diminish the need for outsourcing and can improve patient care. AI can allow for not only diagnosis of patient in areas where healthcare is scarce, but also allow for a good patient experience by resourcing files to find the best treatment for a patient.[134] The ability of AI to adjust course as it goes also allows the patient to have their treatment modified based on what works for them; a level of individualized care that is nearly non-existent in developing countries.[134]
Regulation [ edit ]
Challenges of the clinical use of AI have brought about a potential need for regulations. AI studies need to be completely and transparently reported to have value to inform regulatory approval. Depending on the phase of study, international consensus-based reporting guidelines (TRIPOD+AI,[135] DECIDE-AI,[136] CONSORT-AI[137]) have been developed to provide recommendations on the key details that need to be reported.
A man speaking at the GDPR compliance workshop at the 2019 Entrepreneurship Summit
While regulations exist pertaining to the collection of patient data such as the Health Insurance Portability and Accountability Act in the US (HIPAA) and the European General Data Protection Regulation (GDPR) pertaining to patients within the EU, health care AI is "severely under-regulated worldwide" as of 2025.[128] Unclear is whether healthcare AI can be classified merely as software or as medical device.[128]
United Nations (WHO/ITU) [ edit ]
The ITU-WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) has built a platform known as the ITU-WHO AI for Health Framework for the testing and benchmarking of AI applications in health domain as a joint endeavor of ITU and WHO. As of November 2018, eight use cases were being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.
USA [ edit ]
In 2015, the Office for Civil Rights (OCR) issued rules and regulations to protect the privacy of individuals' health information, requiring healthcare providers to follow certain privacy rules when using AI, to keep a record of how they use AI and to ensure that their AI systems are secure.[139]
In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence.[citation needed] In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes a strategic R&D plan for the subfield of health information technology was in development stages.[citation needed]
In January 2021, the US FDA published a new Action Plan, entitled Artificial Intelligence (AI) /Machine Learning (ML)-Based Software as a Medical Device (SaMD) Action Plan.[140] It layed out the FDA's future plans for regulation of medical devices that would include artificial intelligence in their software with five main actions: 1. Tailored Regulatory Framework for Ai/M:-based SaMD, 2. Good Machine Learning Practice (GMLP), 3. Patient-Centered Approach Incorporating Transparency to Users, 4. Regulatory Science Methods Related to Algorithm Bias & Robustness, and 5. Real-World Performance(RWP). This plan was in direct response to stakeholders' feedback on a 2019 discussion paper also published by the FDA.[140]
Under President Biden the DHSS and the National Institute of Standards and Technology were instructed to develop regulation of healthcare AI.[128] According to the U.S. Department of Health and Human Services, the OCR issued guidance on the ethical use of AI in healthcare in 2021. It outlined four core ethical principles that must be followed: respect for autonomy, beneficence, non-maleficence, and justice. Respect for autonomy requires that individuals have control over their own data and decisions. Beneficence requires that AI be used to do good, such as improving the quality of care and reducing health disparities. Non-maleficence requires that AI be used to do no harm, such as avoiding discrimination in decisions. Finally, justice requires that AI be used fairly, such as using the same standards for decisions no matter a person's race, gender, or income level. As of March 2021, the OCR had hired a Chief Artificial Intelligence Officer (OCAIO) to pursue the "implementation of the HHS AI strategy".[141]
With the second Trump administration deregulation of health AI began on January 20, 2025 with merely voluntary standards for collecting and sharing data, statutory definitions for algorithmic discrimination, automation bias, and equity being cancelled, cuts to NIST and 19% of FDA workforce eliminated.[128]
Europe [ edit ]
Other countries have implemented data protection regulations, more specifically with company privacy invasions. In Denmark, the Danish Expert Group on data ethics has adopted recommendations on "Data for the Benefit of the People". These recommendations are intended to encourage the responsible use of data in the business sector, with a focus on data processing. The recommendations include a focus on equality and non-discrimination with regard to bias in AI, as well as human dignity which is to outweigh profit and must be respected in all data processes.[142]
The European Union has implemented the General Data Protection Regulation (GDPR) to protect citizens' personal data, which applies to the use of AI in healthcare. In addition, the European Commission has established guidelines to ensure the ethical development of AI, including the use of algorithms to ensure fairness and transparency.[143] With GDPR, the European Union was the first to regulate AI through data protection legislation. The Union finds privacy as a fundamental human right, it wants to prevent unconsented and secondary uses of data by private or public health facilities. By streamlining access to personal data for health research and findings, they are able to instate the right and importance of patient privacy.[143] In the United States, the Health Insurance Portability and Accountability Act (HIPAA) requires organizations to protect the privacy and security of patient information. The Centers for Medicare and Medicaid Services have also released guidelines for the development of AI-based medical applications.[144]
In 2025, Europe was leading the USA on AI regulation, while lagging in innovation and at least one California-based biotech company was "engaging the European Medicines Agency earlier in development than previously anticipated to mitigate concerns about the FDA's ability to meet development timelines."[128]
Ethical concerns [ edit ]
While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. AI may also compromise the protection of patients' rights, such as the right to informed consent and the right to medical data protection.[145]
Data collection, privacy - autonomy [ edit ]
In order to effectively train Machine Learning and use AI in healthcare, massive amounts of data must be gathered. Acquiring this data, however, comes at the cost of patient privacy, i.e. autonomy in most cases and is not well received publicly. For example, a survey conducted in the UK estimated that 63% of the population is uncomfortable with sharing their personal data in order to improve artificial intelligence technology.[146] The scarcity of real, accessible patient data is a hindrance that deters the progress of developing and deploying more artificial intelligence in healthcare.
The lack of regulations surrounding AI in the United States has generated concerns about mismanagement of patient data, such as with corporations utilizing patient data for financial gain. For example, as of 2020 Roche, a Swiss healthcare company, was found to have purchased healthcare data for approximately 2 million cancer patients at an estimated total cost of $1.9 billion.[147] Naturally, this generates questions of ethical concern; Is there a monetary price that can be set for data, and should it depend on its perceived value or contributions to science? Is it fair to patients to sell their data? These concerns were addressed in a survey conducted by the Pew Research Center in 2022 that asked Americans for their opinions about the increased presence of AI in their daily lives, and the survey estimated that 37% of Americans were more concerned than excited about such increased presence, with 8% of participants specifically associating their concern with "people misusing AI".[148] Ultimately, the current potential of artificial intelligence in healthcare is additionally hindered by concerns about mismanagement of data collected, especially in the United States.
Technological unemployment [ edit ]
A systematic review and thematic analysis in 2023 showed that most stakeholders including health professionals, patients, and the general public doubted that care involving AI could be empathetic, or fulfill beneficence.[17]
According to a 2019 study, AI can replace up to 35% of jobs in the UK within the next 10 to 20 years.[149] However, of these jobs, it was concluded that AI has not eliminated any healthcare jobs so far. Though if AI were to automate healthcare-related jobs, the jobs most susceptible to automation would be those dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor-to-patient interaction.[149]
Outputs can be incorrect or incomplete and diagnosis and recommendations harm people.[128]
Bias and discrimination [ edit ]
Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated. Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care, i.e. violating the ethical principle of social justice or non-maleficence.[150] A recent scoping review identified 18 equity challenges along with 15 strategies that can be implemented to help address them when AI applications are developed using many-to-many mapping.[151]
There can be unintended bias in algorithms that can exacerbate social and healthcare inequities.[150] Since AI's decisions are a direct reflection of its input data, the data it receives must have accurate representation of patient demographics. For instance, if populations are less represented in healthcare data it is likely to create bias in AI tools that lead to incorrect assumptions of a demographic and impact the ability to provide appropriate care.[152] White males are overly represented in medical data sets.[153] Therefore, having minimal patient data on minorities can lead to AI making more accurate predictions for majority populations, leading to unintended worse medical outcomes for minority populations.[154] Collecting data from minority communities can also lead to medical discrimination. For instance, HIV is a prevalent virus among minority communities and HIV status can be used to discriminate against patients.[153] In addition to biases that may arise from sample selection, different clinical systems used to collect data may also impact AI functionality. For example, radiographic systems and their outcomes (e.g., resolution) vary by provider. Moreover, clinician work practices, such as the positioning of the patient for radiography, can also greatly influence the data and make comparability difficult.[155] However, these biases are able to be eliminated through careful implementation and a methodical collection of representative data.
A final source of algorithmic bias, which has been called "label choice bias", arises when proxy measures are used to train algorithms, that build in bias against certain groups. For example, a widely used algorithm predicted health care costs as a proxy for health care needs, and used predictions to allocate resources to help patients with complex health needs. This introduced bias because Black patients have lower costs, even when they are just as unhealthy as White patients.[156] Solutions to the "label choice bias" aim to match the actual target (what the algorithm is predicting) more closely to the ideal target (what researchers want the algorithm to predict), so for the prior example, instead of predicting cost, researchers would focus on the variable of healthcare needs which is rather more significant. Adjusting the target led to almost double the number of Black patients being selected for the program.
History [ edit ]
Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral.[157][158] While it was designed for applications in organic chemistry, it provided the basis for a subsequent system MYCIN,[159] considered one of the most significant early uses of artificial intelligence in medicine.[159][160] MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however.[161]
The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians.[162] Approaches involving fuzzy set theory,[163] Bayesian networks,[164] and artificial neural networks,[165][166] have been applied to intelligent computing systems in healthcare.
Medical and technological advancements occurring over this half-century period that have enabled the growth of healthcare-related applications of AI to include:
See also [ edit ]
References [ edit ]
| 2022-12-01T00:00:00 |
https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare
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AI in Healthcare: Uses, Examples & Benefits | Built In
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AI in Healthcare: Uses, Examples & Benefits
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https://builtin.com
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[] |
AI is used in healthcare to facilitate disease detection, automate documentation, store and organize health data and accelerate drug discovery and development, ...
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Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.
AI in healthcare shows up in a number of ways, such as finding new links between genetic codes, powering surgery-assisting robots, automating administrative tasks, personalizing treatment options and much more.
Uses for AI in Healthcare Improving medical diagnosis
Speeding up drug discovery
Transforming patient experience
Managing healthcare data
Performing robotic surgery
Put simply, AI is reinventing — and reinvigorating — modern healthcare through machines that can predict, comprehend, learn and act.
What Is AI in Healthcare?
AI in healthcare refers to the use of machine learning, natural language processing, deep learning and other AI technologies to enhance the experiences of both healthcare professionals and patients. The data-processing and predictive capabilities of AI enable health professionals to better manage their resources and take a more proactive approach to various aspects of healthcare.
With these technologies, doctors can then make quicker and more accurate diagnoses, health administrators can locate electronic health records faster and patients can receive more timely and personalized treatments.
More on Healthcare and TechnologyTop Healthtech Companies to Know
Examples of AI in Healthcare
To give you a better understanding of the rapidly evolving field, we rounded up some examples and use cases of AI in healthcare.
AI in Medical Diagnosis
Every year, roughly 400,000 hospitalized patients suffer preventable harm, with 100,000 deaths. In light of that, the promise of improving the diagnostic process is one of AI’s most exciting healthcare applications. Incomplete medical histories and large caseloads can lead to deadly human errors. Immune to those variables, AI can predict and diagnose disease at a faster rate than most medical professionals.
AI in Drug Discovery
The drug development industry is bogged down by skyrocketing development costs and research that takes thousands of human hours. Putting each drug through clinical trials costs an estimated average of $1.3 billion, and only 10 percent of those drugs are successfully brought to market. Due to breakthroughs in technology, AI is speeding up this process by helping design drugs, predicting any side effects and identifying ideal candidates for clinical trials.
AI in Patient Experience
AI can be used to support digital communications, offering schedule reminders, tailored health tips and suggested next steps to patients. The ability of AI to aid in health diagnoses also improves the speed and accuracy of patient visits, leading to faster and more personalized care. And efficiently providing a seamless patient experience allows hospitals, clinics and physicians to treat more patients on a daily basis.
AI in Healthcare Data Management
Highly valuable information can sometimes get lost among the forest of trillions of data points. Additionally, the inability to connect important data points slows the development of new drugs, preventative medicine and proper diagnosis. Because of its ability to handle massive volumes of data, AI breaks down data silos and connects in minutes information that used to take years to process. This can reduce the time and costs of healthcare administrative processes, contributing to more efficient daily operations and patient experiences.
AI in Robotic Surgery
Hospitals use AI and robots to help with everything from minimally invasive procedures to open heart surgery. Surgeons can control a robot’s mechanical arms while seated at a computer console as the robot gives the doctor a three-dimensional, magnified view of the surgical site. The surgeon then leads other team members who work closely with the robot through the entire operation. Robot-assisted surgeries have led to fewer surgery-related complications, less pain and a quicker recovery time.
Companies Using AI in Healthcare
These are some of the companies paving the way for healthcare innovation by applying AI technology.
Location: New York, New York
EliseAI specializes in conversational AI solutions. In the healthcare space, EliseAI offers AI-powered technology that can automate administrative tasks like appointment scheduling and sending payment reminders. Its AI capabilities engage patients across SMS, voice, email and web chat formats.
Location: San Mateo, California
Evidation’s mobile app supports users’ health through rewards and education content. It also gives them the option of participating in health research for life sciences companies, government agencies and academic institutions. The company uses AI to support its research partners, developing solutions for applications like notifying users who report flu systems and are in the right geographic location about how to join a clinical trial for a flu treatment.
Location: Boston, Massachusetts
Cohere Health uses AI and machine learning to revolutionize prior authorization processes to ensure patients can access care swiftly. Through its Cohere Unify Platform, health plans can proactively create data-driven care paths, leading to pre-approval for services. By integrating real-time analytics, clinical intelligence and responsible AI, Cohere aligns patients, healthcare providers and health plans. It aims to facilitate stress-free experiences to deliver efficient, quality, cost-effective care.
Location: New York, New York
Flatiron Health is a cloud-based SaaS company specializing in cancer care, offering oncology software that connects cancer centers nationwide to improve treatments and accelerate research. Using advanced technology, including artificial intelligence, it advances oncology by connecting community oncologists, academics, hospitals and life science researchers, providing integrated patient population data and business intelligence analytics. By leveraging billions of data points from cancer patients, Flatiron Health enables stakeholders to gain new insights and enhance patient care.
Location: Evanston, Illinois
Global consulting firm ZS specializes in providing strategic support to businesses across various sectors, with a particular focus on healthcare, leveraging its expertise in AI, sales, marketing, analytics and digital transformation. ZS helps clients navigate complex challenges within industries such as medical technology, life sciences, health plans and pharmaceuticals, using advanced AI and analytics tools.
Location: New York, New York
Healthee uses AI to power its employee benefits app, which businesses rely on to help their team members effectively navigate the coverage and medical treatment options available to them. It includes a virtual healthcare assistant known as Zoe that offers Healthee users personalized answers to benefits-related questions.
Location: New York, New York
Pfizer uses AI to aid its research into new drug candidates for treating various diseases. For example, the company used AI and machine learning to support the development of a Covid-19 treatment called PAXLOVID. Scientists at Pfizer are able to rely on modeling and simulation to identify compounds that have the highest likelihood of being effective treatment candidates so they can narrow their efforts.
Location: Tokyo, Japan
As a global pharmaceutical company, Takeda works to develop treatments and vaccines to address conditions ranging from celiac disease and Parkinson’s disease to rare autoimmune disorders and dengue. Takeda’s outline for sustainably and responsibly adopting AI into its operations explains that the company uses the technology for applications like developing new medicines and optimizing treatments already in use.
Location: Fort Collins, Colorado
Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company’s deep learning platform analyzes unstructured medical data — radiology images, blood tests, EKGs, genomics, patient medical history — to give doctors better insight into a patient’s real-time needs.
Location: Austin, Texas
Babylon is on a mission to re-engineer healthcare by shifting the focus away from caring for the sick to helping prevent sickness, leading to better health and fewer health-related expenses. The platform features an AI engine created by doctors and deep learning scientists that operates an interactive symptom checker, using known symptoms and risk factors to provide the most informed and up-to-date medical information possible.
Location: Burlington, Massachusetts
Butterfly Network designs AI-powered probes that connect to a mobile phone, so healthcare personnel can conduct ultrasounds in a range of settings. Both the iQ3 and IQ+ products provide high-quality images and extract data for fast assessments. With the ability to create and analyze 3D visualizations, Butterfly Network’s tools can be used for anesthesiology, primary care, emergency medicine and other areas.
Location: Palo Alto, California
CloudMedX uses machine learning to generate insights for improving patient journeys throughout the healthcare system. The company’s technology helps hospitals and clinics manage patient data, clinical history and payment information by using predictive analytics to intervene at critical junctures in the patient care experience. Healthcare providers can use these insights to efficiently move patients through the system.
Location: Boston, Massachusetts
Biofourmis connects patients and health professionals with its cloud-based platform to support home-based care and recovery. The company’s platform integrates with mobile devices and wearables, so teams can collect AI-driven insights, message patients when needed and conduct virtual visits. This way, hospitals can release patients earlier and ensure a smoother transition while remotely monitoring their progress.
Location: San Mateo, California
Caption Health combines AI and ultrasound technology for early disease identification. AI guides providers through the ultrasound process in real time to produce diagnostic-quality images that the software then helps to interpret and assess.
Location: Copenhagen, Denmark
Corti’s platform leverages AI to improve the operations and practices of emergency medical services personnel. A suite of Corti features automatically summarizes emergency calls, speeds up documentation and tracks employee performance. By compiling and analyzing this data, Corti can deliver insights to help teams pinpoint inefficiencies, offer employees tailored feedback and update any call guidelines as needed.
Location: San Francisco, California
Atomwise uses AI to tackle serious diseases, including Ebola and multiple sclerosis. The company’s neural network, AtomNet, helps predict bioactivity and identify patient characteristics for clinical trials. Atomwise’s AI technology screens between 10 and 20 million genetic compounds each day and can reportedly deliver results 100 times faster than traditional pharmaceutical companies.
Location: South San Francisco, California
Freenome uses AI in screenings, diagnostic tests and blood work to test for cancer. By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments.
Location: Salt Lake City, Utah
Recursion’s operating system accelerates drug discovery and development by generating and analyzing large amounts of in-house biological and chemical data. During experiments, Recursion relies on hardware systems, microscopes and continuous video feeds to collect data for its OS to review. The company has also partnered with NVIDIA to apply generative AI to its methods, making drug development even faster.
Location: San Francisco, California
Insitro specializes in human disease biology, combining generative AI and machine learning to spearhead medicine development. The company generates phenotypic cellular data and gathers clinical data from human cohorts for deep learning and machine learning models to comb through. Based on this information, Insitro’s technology can spot patterns in genetic data and build disease models to spur the discovery of new medicines.
Location: New York, New York
Owkin leverages AI technology for drug discovery and diagnostics with the goal of enhancing cancer treatment. The company’s AI tools help identify new drug targets, recommend possible drug combinations and suggest additional diseases that a drug can be repurposed to treat. Owkin also produces RlapsRisk, a diagnostic tool for assessing a breast cancer patient’s risk of relapse, and MSIntuit, a tool that assists with screening for colorectal cancer.
Location: Toronto, Ontario
Deep Genomics’ AI platform helps researchers find candidates for developmental drugs related to neuromuscular and neurodegenerative disorders. Finding the right candidates during a drug’s development statistically raises the chances of successfully passing clinical trials while also decreasing time and cost to market.
Location: Armonk, New York
Once known as a Jeopardy-winning supercomputer, IBM’s Watson now helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients and improve treatment. Watson applies its skills to everything from developing personalized health plans to interpreting genetic testing results and catching early signs of disease.
Location: Houston, Texas
InformAI offers a suite of AI products for the healthcare field. Its RadOncAI tool uses AI to create a radiation therapy plan, homing in on tumors while limiting cancer patients’ exposure as much as possible. Meanwhile, TransplantAI evaluates donor and recipient data to determine promising matches and support successful organ transplants. And InformAI’s SinusAI product helps health teams more quickly detect sinus diseases.
Location: San Francisco, California
Komodo Health has built the “industry’s largest and most complete database of de-identified, real-world patient data,” known as the Healthcare Map. This Map tracks individual patient interactions across the healthcare system, applying AI and machine learning to extract data related to individuals or larger demographics. With this information, healthcare professionals can develop more complete patient profiles while also using categories like race and ethnicity to factor social inequities into a patient’s health history.
Location: Philadelphia, Pennsylvania
Oncora Medical aids oncologists in cancer research and prevention. During patient consultations, the company’s platform automates notetaking and locates important patient details from past records, saving oncologists time. Oncora’s platform also comes equipped with machine learning models that can identify high-risk individuals and determine when patients are eligible to participate in clinical trials.
Location: New York, New York
AiCure helps healthcare teams ensure patients are following drug dosage instructions during clinical trials. Supplementing AI and machine learning with computer vision, the company’s mobile app tracks when patients aren’t taking their medications and gives clinical teams time to intervene. In addition, AiCure provides a platform that gleans insights from clinical data to explain patient behavior, so teams can study how patients react to medications.
Location: Boston, Massachusetts
PathAI develops machine learning technology to assist pathologists in making more accurate diagnoses. The company’s goals include reducing errors in cancer diagnosis and developing methods for individualized medical treatment. PathAI worked with drug developers like Bristol-Myers Squibb and organizations like the Bill & Melinda Gates Foundation to expand its AI technology into other healthcare industries.
Location: Menlo Park, California
GRAIL leverages AI to detect cancer in its early stages. With a single blood test, the company’s Galleri test screens over 100,000 DNA regions for cancer signals. If it detects cancerous cells, the test can predict the tissue or organ associated with the cancer. GRAIL intends for its test to become a routine screening for cancer along with other comprehensive detection methods.
Location: Boston, Massachusetts
With its early detection platform for cognitive assessments, Linus Health is on a mission to modernize brain health. Its proprietary assessment technology DCTclock takes the gold standard pen-and-paper clock drawing test for early signs of cognitive impairment and digitizes it, bringing together the most recent advances in neuroscience and AI to analyze over 100 metrics that reflect the patient’s cognitive function.
Location: San Francisco, California
In healthcare, delays can mean the difference between life and death, so Viz.ai helps care teams react faster with AI-powered healthcare solutions. The company’s AI products can detect issues and notify care teams quickly, enabling providers to discuss options and provide faster treatment decisions, thus saving lives.
Location: Los Angeles, California
Regard uses AI technology to diagnose patients. The company describes its automated system to be the clinical “co-pilot” to electronic medical records (EMRs). The data from EMRs is synthesized to discover a diagnosis. Additionally, healthcare providers receive specific recommendations about patient care. The system also updates patient documents automatically to reduce burnout among healthcare workers.
Location: Boston, Massachusetts
Developed by a team out of Harvard Medical School, Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. Here’s how it works: a chatbot listens to a patient’s symptoms and health concerns, then guides that patient to the correct care based on its diagnosis.
Location: Boston, Massachusetts
Beth Israel Deaconess Medical Center used AI for diagnosing potentially deadly blood diseases at an early stage. Doctors developed AI-enhanced microscopes to scan for harmful bacteria like E. coli and staphylococcus in blood samples at a faster rate than is possible using manual scanning. The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95 percent accuracy.
Location: Cambridge, Massachusetts
Iterative Health applies AI to gastroenterology to improve disease diagnosis and treatment. The company’s AI recruitment service uses computational algorithms to automate the process of identifying patients who are eligible to be potential candidates for inflammatory bowel disease clinical trials. Iterative Health also produces SKOUT, a tool that uses AI to help doctors identify potentially cancerous polyps.
Location: Peoria, Illinois
VirtuSense uses AI sensors to track a patient’s movements so that providers and caregivers can be notified of potential falls. The company’s products include VSTAlert, which can predict when a patient intends to stand up and notify appropriate medical staff, and VST Balance, which employs AI and machine vision to analyze a person’s risk of falling within the next year.
Location: Fully Remote
Cleerly makes AI technology to improve cardiovascular care. The company’s AI-enabled digital care platform measures and analyzes atherosclerosis, which is a buildup of plaque in the heart’s arteries. The technology is able to determine an individual’s risk of having a heart attack and recommend a personalized treatment plan.
Location: Bagsværd, Denmark
Novo Nordisk is a pharmaceutical and biotech company collaborating with Valo Health to develop new treatments for cardiometabolic diseases. The partnership seeks to make discovery and development faster by using Valo’s AI-powered computational platform, patient data and human tissue modeling technology.
Location: New Haven, Connecticut
BioXcel Therapeutics uses AI to identify and develop new medicines in the fields of immuno-oncology and neuroscience. Additionally, the company’s drug re-innovation program employs AI to find new applications for existing drugs or to identify new patients.
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Location: Boston, Massachusetts
Valo uses artificial intelligence to achieve its mission of transforming the drug discovery and development process. With its Opal Computational Platform, Valo collects human-centric data to identify common diseases among a specific phenotype, genotype and other links, which eliminates the need for animal testing. The company then establishes the molecule design and clinical development.
Location: Cambridge, Massachusetts
Combining AI, the cloud and quantum physics, XtalPi’s ID4 platform predicts the chemical and pharmaceutical properties of small-molecule candidates for drug design and development. The company’s investors have included Google, Tencent and Sequoia Capital.
Location: London, England
The primary goal of BenevolentAI is to get the right treatment to the right patients at the right time by using AI to produce a better target selection and provide previously undiscovered insights through deep learning. BenevolentAI works with major pharmaceutical groups to license drugs, while also partnering with charities to develop easily transportable medicines for rare diseases.
Location: Menlo Park, California
Deepcell uses artificial intelligence and microfluidics to develop technology for single-cell morphology. The company’s platform has a variety of applications, including cancer research, cell therapy and developmental biology.
Location: Austin, Texas
With the goal of improving patient care, Iodine Software is creating AI-powered and machine-learning solutions for mid-revenue cycle leakages, like resource optimization and increased response rates. The company’s CognitiveML product discovers client insights, ensuriodes documentation accuracy and highlights missing information.
Location: New York, New York
Kaia Health operates a digital therapeutics platform that features live physical therapists to provide people care within the boundaries of their schedules. The platform includes personalized programs with case reviews, exercise routines, relaxation activities and learning resources for treating chronic back pain and COPD. Kaia Health also features a PT-grade automated feedback coach that uses AI technology.
Location: New York, New York
Spring Health offers a mental health benefit solution employers can adapt to provide their employees with the resources to keep their mental health in check. The technology works by collecting a comprehensive dataset from each individual and comparing that against hundreds of thousands of other data points. The platform then uses a machine learning model to match people with the right specialist for either in-person care or telehealth appointments.
Location: Mountain View, California
Twin Health’s holistic method seeks to address and potentially reverse chronic conditions like Type 2 Diabetes through a mixture of IoT tech, AI, data science, medical science and healthcare. The company created the Whole Body Digital Twin — a digital representation of human metabolic function built around thousands of health data points, daily activities and personal preferences.
Location: Mountain View, California
Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company’s automated platform can prioritize patient illness and injury and tracks hospital waiting times to help hospitals and health systems optimize care delivery.
Location: Cleveland, Ohio
The Cleveland Clinic teamed up with IBM on the Discovery Accelerator, an AI-infused initiative focused on faster healthcare breakthroughs. The joint center is building an infrastructure that supports research in areas such as genomics, chemical and drug discovery and population health. The collaboration employs big data medical research for the purpose of innovating patient care and approaches to public health threats.
Location: Baltimore, Maryland
Johns Hopkins Hospital partnered with GE Healthcare to use predictive AI techniques to improve the efficiency of patient operational flow. A task force, augmented with AI, quickly prioritized hospital activity to benefit patients. Since implementing the program, the facility has assigned patients admitted to the emergency department to beds 38 percent faster.
Location: New York, New York
One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management. The One Drop Premium app allows people to manage their conditions head first, offering interactive coaching from real-world professionals, predictive glucose readings powered by AI and data science, learning resources and daily tracking of readings taken from One Drop’s Bluetooth-enabled glucose reader and other devices.
Location: Menlo Park, California
Subtle Medical uses AI to enhance images for radiology departments. The SubtlePET and SubtleMR products work with the machines a facility already uses to speed up MRI and PET scans while reducing image noise. The software has the potential to shrink wait times by scanning more patients each day.
Location: New York, New York
Twill describes itself as “The Intelligent Healing Company,” delivering digital healthcare products and partnering with enterprises, pharma companies and health plans to develop products using its Intelligent Healing Platform. The company uses AI to tailor personalized care tracks for managing medical conditions like multiple sclerosis and psoriasis. These individualized programs can include digital therapeutics, care communities and coaching options.
Location: San Francisco, California
Augmedix offers a suite of AI-enabled medical documentation tools for hospitals, health systems, individual physicians and group practices. The company’s products use natural language processing and automated speech recognition to save users time, increase productivity and improve patient satisfaction.
Location: Indianapolis, Indiana
Greenlight Guru, a medical technology company, uses AI in its search engine to detect and assess security risks in network devices. The company specializes in developing medical software, and its search engine leverages machine learning to aggregate and process industry data. Meanwhile, its risk management platform provides auto-calculated risk assessments, among other services.
Location: Chicago, Illinois
Tempus uses AI to sift through the world’s largest collection of clinical and molecular data to personalize healthcare treatments. The company develops AI tools that give physicians insights into treatments and cures, aiding in areas like radiology, cardiology, and neurology.
Location: Austin, Texas
ClosedLoop.ai is an end-to-end platform that uses AI to discover at-risk patients and recommend treatment options. Through the platform, healthcare organizations can receive personalized data about patients’ needs while collecting looped feedback, outreach and engagement strategies and digital therapeutics. The platform can be used by healthcare providers, payers, pharma and life science companies.
Location: Boston, Massachusetts
Beacon Biosignals aims to treat neurological and psychiatric diseases through its EEG analytics platform, which utilizes portal reporting, standardized neurobiomarkers and machine learning algorithms that “increase the probability of success at each stage of drug development.” Additionally, population stratification is used to identify various patient populations.
Location: Philadelphia, Pennsylvania
Proscia is a digital pathology platform that uses AI to detect patterns in cancer cells. The company’s software helps pathology labs eliminate bottlenecks in data management and uses AI-powered image analysis to connect data points that support cancer discovery and treatment.
Location: Mountain View, California
H2O.ai’s AI analyzes data throughout a healthcare system to mine, automate and predict processes. It has been used to predict ICU transfers, improve clinical workflows and pinpoint a patient’s risk of hospital-acquired infections. Using the company’s AI to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates.
Location: South San Francisco, California
AKASA’s AI platform helps healthcare providers streamline workflows by automating administrative tasks to allow staff to focus where they’re needed. The automation can be customized to meet a facility’s particular needs and priorities, while maintaining accuracy for managing claims, payments and other elements of the revenue cycle.
Location: Waltham, Massachusetts
Vicarious Surgical combines virtual reality with AI-enabled robots so surgeons can perform minimally invasive operations. Using the company’s technology, surgeons can virtually shrink and explore the inside of a patient’s body in detail. Vicarious Surgical’s technology concept prompted former Microsoft chief Bill Gates to invest in the company.
Location: Madison, Wisconsin
The Accuray CyberKnife system uses AI and robotics to precisely treat cancerous tumors. The technology lets providers personalize stereotactic radiosurgery and stereotactic body radiation therapy for each patient. Using the robot’s real-time tumor tracking capabilities, doctors and surgeons can treat affected areas rather than the whole body.
Location: Sunnyvale, California
The first robotic surgery assistant approved by the FDA, Intuitive’s da Vinci platforms feature cameras, robotic arms and surgical tools to aid in minimally invasive procedures. Da Vinci platforms constantly take in information and provide analytics to surgeons to improve future procedures. Da Vinci has assisted in over 10 million operations.
Location: Pittsburgh, Pennsylvania
The Robotics Institute at Carnegie Mellon University developed HeartLander, a miniature mobile robot designed to facilitate therapy on the heart. Under a physician’s control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart and administers therapy.
Location: Eindhoven, Netherlands
Microsure’s robots help surgeons overcome their human physical limitations. The company’s motion stabilizer system is intended to improve performance and precision during surgical procedures. Its MUSA surgical robot, developed by engineers and surgeons, can be controlled via joysticks for performing microsurgery.
Location: Boston, Massachusetts
Laudio works to help frontline managers build high-performing teams. The company’s technology leverages AI-powered recommendations to drive targeted managerial actions that help streamline workflows for frontline healthcare workers. Laudio’s goal is to help frontline teams improve efficiency, employee engagement and patient experiences.
Location: Framingham, Massachusetts
Definitive Healthcare offers healthcare intelligence software that converts third-party data, secondary and proprietary research into actionable insights. It aims to deliver an organized, searchable and user-friendly platform. The company helps businesses in the healthcare space to market their products to their target audiences.
Location: New York, New York
Formation Bio is a pharmaceutical company that uses AI to develop new and existing drugs. The company uses AI throughout development, manufacturing and marketing. It aims to accelerate drug development pipelines and get new products to patients more efficiently.
Location: Denver, Colorado
Strive Health aims to transform kidney disease care through services and technology that prioritize early identification and responses that help lower overall costs. It provides its clients with local providers who use predictive and comparative data to design home-first dialysis options and comprehensive care plans. The Kidney Heroes™, who include nurses, social workers, nurse practitioners, dietitians and care coordinators are trained to understand all intricacies of kidney disease and provide specialized care.
Location: Rosemont, Illinois
IMO Health incorporates AI into its solutions for improving the quality of clinical data. The technology can be used to maintain accurate surgical dictionaries and ensure documentation aligns with regulatory requirements. IMO Health’s offerings have uses for a variety of organizations, including health plans, healthcare providers and clinical research programs.
Artera offers a patient communication platform using AI models and infrastructure. Its workflows are built to increase patient access, reduce staff response time and improve staff to patient ratios. The company’s generative AI and classification models are meant to improve inbox management by moving the highest priority messages to the top.
Location: Boston, Massachusetts
Healthcare providers use Arcadia’s data platform to access insights that can help them improve operational efficiency and deliver proactive, informed patient care. Its technology comes with a generative AI assistant that unifies data from various sources to offer context and recommendations across areas like financial risk, compliance and care management.
Location: Santa Monica, California
TigerConnect offers a platform designed to unify communications across healthcare operations, with the goal of enabling streamlined collaboration and improving the patient care experience. The technology comes with AI-powered features that providers can use to simplify daily tasks like scheduling.
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Frequently Asked Questions
What is AI in healthcare? AI in healthcare is the use of machine learning, natural language processing, deep learning and other types of AI technology in the health field. These technologies are intended to improve health professionals’ capabilities and performance while enhancing the patient experience.
How is AI used in healthcare? AI is used in healthcare to facilitate disease detection, automate documentation, store and organize health data and accelerate drug discovery and development, among other use cases.
What companies are using AI in healthcare? Some healthcare companies using AI are EliseAI, Cohere Health, Pfizer, Butterfly Network and Novo Nordisk.
| 2022-12-01T00:00:00 |
https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare
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Artificial Intelligence in Healthcare - MIT xPRO
|
Artificial Intelligence in Healthcare
|
https://xpro.mit.edu
|
[] |
This program is designed to equip you with the skills to broaden your understanding of the applications of AI-based technologies in healthcare.
|
With its ability to accurately predict diseases at early stages, artificial intelligence (AI) plays an increasingly important role in today’s healthcare industry. The global AI-based healthcare solutions market size is expected to reach USD 208 billion by 2030 (Source: Research and Markets). However, 46% of clinicians report a shortage of people with AI-implementation skills in their organization (Source: World Economic Forum). MIT xPRO’s AI in Healthcare: Fundamentals and Applications program is designed to help healthcare leaders like you understand AI technologies and ways of leveraging them to develop innovative solutions that make a difference in patient treatment.
| 2022-12-01T00:00:00 |
https://xpro.mit.edu/courses/course-v1:xPRO+AIHCx+R1/
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"date": "2024/10/01",
"position": 23,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 23,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 21,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 25,
"query": "artificial intelligence healthcare"
}
] |
|
AI for healthcare - Imperial College London
|
AI for healthcare
|
https://www.imperial.ac.uk
|
[
"Ian Mundell"
] |
AI offers a real opportunity in healthcare, not only to automate some of the problem-solving carried out by doctors and other medical professionals.
|
Pooling clinical experience
Applying AI to medical imaging is a focus for Professor Daniel Rueckert, head of the Department of Computing and leader of the Biomedical Image Analysis group (BioMedIA).
He describes imaging as a pipeline, beginning with a patient being scanned and ending with clinically useful information. “Our group is applying AI at each stage of the pipeline,” he explains. “We are using AI to acquire images faster and with better quality, to extract information from the images, and to take this information and turn it into a diagnosis or a prediction about the patient.”
Even an experienced doctor may not have seen all types of cancer. Algorithms can pool the data from hundreds of thousands of rare cases.
For purely visual tasks, the AI is learning to emulate what human experts such as radiologists do when looking for signs of a disease such as cancer. “Humans have a very good perceptual system, and radiologists are trained to spot many different types of diseases. But when it comes to making predictions about the patient, even an experienced doctor may not have seen all types of cancer, or only a few cases of the rarest cancers.”
This is where AI can make a difference. “The learning algorithms can pool the data from hundreds of hospitals, with hundreds of thousands of these rare cases, and support the diagnosis of a clinician who will not have had this experience.”
This approach is also being applied to prenatal ultrasound screening in the iFIND project, a collaboration with clinicians at King’s College London. “One of the challenges of ultrasound is that certain foetal abnormalities are very difficult to spot, even for experienced sonographers,” says Professor Rueckert. There is also a postcode lottery in diagnosis that exists because of uneven training and human resources available across the country.
“AI offers a great way of improving the quality of screening by having the AI operate as a second observer. You still have the sonographer doing the examination, but the AI system can alert the sonographer to things they might need to pay closer attention to.”
| 2022-12-01T00:00:00 |
https://www.imperial.ac.uk/stories/healthcare-ai/
|
[
{
"date": "2022/12/01",
"position": 25,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
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"query": "AI healthcare"
},
{
"date": "2023/01/01",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 16,
"query": "AI healthcare"
},
{
"date": "2023/02/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/07/01",
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"query": "AI healthcare"
},
{
"date": "2023/08/01",
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"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 19,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
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"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 27,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 20,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 25,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 20,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 33,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 26,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 19,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 20,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 27,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 29,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 26,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 33,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 18,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 32,
"query": "AI healthcare"
}
] |
|
Artificial Intelligence and Machine Learning (AI/ML)-Enabled ... - FDA
|
Artificial Intelligence-Enabled Medical Devices
|
https://www.fda.gov
|
[] |
The AI/ML-Enabled Medical Device List is a resource intended to identify AI/ML-enabled medical devices that are authorized for marketing in the United States.
|
The FDA encourages the development of innovative, safe, and effective medical devices, including devices that incorporate artificial intelligence (AI).
The AI-Enabled Medical Device List is a resource intended to identify AI-enabled medical devices that are authorized for marketing in the United States. Digital health innovators can refer to this list to gain insights into the current device landscape and regulatory expectations, which can help foster innovation and ensure public safety. This list can also provide transparency for healthcare providers and patients to clearly identify when medical devices use AI technologies.
Contents of the AI-Enabled Medical Devices List:
The devices in this list have met the FDA’s applicable premarket requirements, including a focused review of the device’s overall safety and effectiveness, which includes an evaluation of study appropriateness for the device’s intended use and technological characteristics.
A direct link to the FDA’s database entry of an AI-enabled medical device is provided. The database entry contains releasable information, such as summaries of safety and effectiveness. Note, the summaries are not all inclusive and do not include most of the information that may be submitted in an application.
The list is not a comprehensive resource of AI-enabled medical devices. Instead, the list includes AI-enabled medical devices that were identified primarily based on the use of AI-related terms in the summary descriptions of their marketing authorization document and/or the device’s classification. The set of AI terms is based on the FDA Digital Health and Artificial Intelligence Glossary.
To support transparency in the use of modern AI technologies, the FDA will explore methods to identify and tag medical devices that incorporate foundation models encompassing a wide range of AI systems, from large language models (LLMs) to multimodal architectures. This identification will help innovators, healthcare providers, and patients recognize when LLM-based functionality is present in a medical device. To facilitate the FDA’s development of methods to identify AI-enabled medical devices more easily, including identifying those devices incorporating LLM-based functionality in a future update of this list, sponsors are encouraged to include appropriate information in their public summaries.
This list will continue to be updated periodically. AI-enabled medical devices that have received authorization but for which decision summaries have not been published within the data collection period will be incorporated into a subsequent update.
Send questions or feedback on this list to [email protected].
AI-Enabled Medical Devices List
Devices are listed in reverse chronological order by Date of Final Decision. To change the sort order, click the arrows in the column headings.
Use the Submission Number link to display the approval, authorization, or clearance information for the device in the appropriate FDA database. The database page will include a link to the FDA's publicly available information.
Download a CSV File Download an Excel File Save as XML File*
*To save the XML file, right click and save the file to your computer and open in the appropriate program.
| 2025-07-10T00:00:00 |
2025/07/10
|
https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
|
[
{
"date": "2022/12/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 31,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 30,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 43,
"query": "artificial intelligence healthcare"
}
] |
What is Artificial Intelligence in Medicine? | IBM
|
What is artificial intelligence in medicine?
|
https://www.ibm.com
|
[] |
Artificial intelligence in medicine is the use of machine learning models to help process medical data and give medical professionals important insights.
|
Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.
Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.
The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients.
The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.
| 2022-12-01T00:00:00 |
https://www.ibm.com/think/topics/artificial-intelligence-medicine
|
[
{
"date": "2022/12/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
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},
{
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{
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{
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{
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},
{
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},
{
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"position": 45,
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},
{
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"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 37,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 45,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
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},
{
"date": "2023/12/01",
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},
{
"date": "2023/12/01",
"position": 35,
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},
{
"date": "2024/01/01",
"position": 49,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 35,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 49,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 30,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 38,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 47,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 37,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 48,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 37,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 49,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 35,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 49,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 36,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 47,
"query": "AI healthcare"
}
] |
|
Artificial Intelligence (AI) in Healthcare Market Growth, Drivers, and ...
|
Artificial Intelligence (AI) in Healthcare Market Growth, Drivers, and Opportunities
|
https://www.marketsandmarkets.com
|
[] |
The global Artificial Intelligence (AI) in healthcare market, valued at US$14.92 billion in 2024, stood at US$21.66 billion in 2025 and is projected to advance ...
|
The study involved significant activities in estimating the current size of the Artificial Intelligence (AI) in healthcare market. Exhaustive secondary research was done to collect information on the Artificial Intelligence (AI) in healthcare market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain using primary research. Different approaches, such as top-down and bottom-up, were employed to estimate the total market size. After that, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the Artificial Intelligence (AI) in healthcare market.
Secondary Research
This research study involved the wide use of secondary sources, directories, and databases such as Dun & Bradstreet, Bloomberg Businessweek, and Factiva; white papers, annual reports, and companies’ house documents; investor presentations; and the SEC filings of companies. The market for the companies offering Artificial Intelligence (AI) in healthcare solutions is arrived at by secondary data available through paid and unpaid sources, analyzing the product portfolios of the major companies in the ecosystem, and rating the companies by their performance and quality. Various sources were referred to in the secondary research process to identify and collect information for this study. The secondary sources include annual reports, press releases, investor presentations of companies, white papers, journals, certified publications, and articles from recognized authors, directories, and databases.
Various secondary sources were referred to in the secondary research process to identify and collect information related to the study. These sources included annual reports, press releases, investor presentations of Artificial Intelligence (AI) in healthcare vendors, forums, certified publications, and whitepapers. The secondary research was used to obtain critical information on the industry’s value chain, the total pool of key players, market classification, and segmentation from the market and technology-oriented perspectives.
Primary Research
In the primary research process, various sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. Primary sources are mainly industry experts from the core and related industries and preferred suppliers, manufacturers, distributors, technology developers, researchers, and organizations related to all segments of this industry’s value chain. In-depth interviews were conducted with various primary respondents, including key industry participants, subject-matter experts (SMEs), C-level executives of key market players, and industry consultants, among other experts, to obtain and verify the critical qualitative and quantitative information as well as assess prospects.
Primary research was conducted to identify segmentation types, industry trends, key players, and key market dynamics such as drivers, restraints, opportunities, challenges, industry trends, and strategies adopted by key players.
After the complete market engineering (calculations for market statistics, market breakdown, market size estimations, market forecasting, and data triangulation), extensive primary research was conducted to gather information and verify and validate the critical numbers arrived at.
In the complete market engineering process, the top-down and bottom-up approaches and several data triangulation methods were extensively used to perform the market estimation and market forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to list the key information/insights throughout the report.
Breakdown of the Primary Respondents:
Note: Other designations include sales managers, marketing managers, and product managers.
Note: Tiers are defined based on a company’s total revenue as of 2024: Tier 1 = >USD 1 billion, Tier 2 = USD 500 million to USD 1 billion, and Tier 3 = < USD 500 million.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation The market size estimates and forecasts provided in this study are derived through a mix of the bottom-up approach (revenue share analysis of leading players) and top-down approach (assessment of utilization/adoption/penetration trends by offering, function, application, deployment, tool, end user, and region). Data Triangulation After arriving at the overall market size—using the market size estimation processes—the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and sub-segment, data triangulation and market breakdown procedures were employed, wherever applicable. The data was triangulated by studying various factors and trends from both the demand and supply sides in the Artificial Intelligence (AI) in healthcare market.
| 2022-12-01T00:00:00 |
https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html
|
[
{
"date": "2022/12/01",
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"query": "artificial intelligence healthcare"
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{
"date": "2023/01/01",
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},
{
"date": "2023/02/01",
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},
{
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{
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{
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{
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},
{
"date": "2023/12/01",
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},
{
"date": "2023/12/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
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"query": "artificial intelligence healthcare"
},
{
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{
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{
"date": "2024/09/01",
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"query": "AI healthcare"
},
{
"date": "2024/10/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 38,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 38,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 33,
"query": "artificial intelligence healthcare"
}
] |
|
Artificial intelligence (AI) in healthcare | Google Cloud
|
Artificial intelligence (AI) in healthcare
|
https://cloud.google.com
|
[] |
AI is transforming the healthcare industry, revolutionizing the way care is delivered, research is conducted, and administrative tasks are handled.
|
AI is expected to play an increasingly vital role in healthcare, with advancements in:
Personalized medicine: AI will enable precision medicine, tailoring treatments and therapies to individual patients based on their unique genetic makeup and health history.
Wearable devices and remote monitoring: AI-powered wearable devices and remote monitoring systems will enhance patient data collection, enabling early detection and proactive care.
Virtual health assistants: AI-based virtual assistants will provide patients with personalized health information, reminders, and support outside of traditional healthcare settings.
| 2022-12-01T00:00:00 |
https://cloud.google.com/use-cases/ai-in-healthcare
|
[
{
"date": "2022/12/01",
"position": 36,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 25,
"query": "AI healthcare"
},
{
"date": "2023/01/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
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},
{
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},
{
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},
{
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/07/01",
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"query": "AI healthcare"
},
{
"date": "2023/08/01",
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"query": "AI healthcare"
},
{
"date": "2023/09/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
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"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 37,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 24,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 36,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 26,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 25,
"query": "AI healthcare"
},
{
"date": "2024/05/01",
"position": 33,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 23,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 33,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 26,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 33,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 27,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 33,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 34,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 27,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 35,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 27,
"query": "AI healthcare"
}
] |
|
Health Care Artificial Intelligence Code of Conduct - NAM
|
Health Care Artificial Intelligence Code of Conduct
|
https://nam.edu
|
[
"Grace Cordovano",
"Selwyn Vickers",
"Vardit Ravitsky"
] |
A guiding framework to ensure that AI algorithms and their application in health, health care, and biomedical science perform accurately, safely, reliably, and ...
|
About
Toward a Code of Conduct Framework for Artificial Intelligence in Health, Health Care, and Biomedical Science
The Artificial Intelligence Code of Conduct (AICC) project is a pivotal initiative of the NAM, aimed at providing a guiding framework to ensure that AI algorithms and their application in health, health care, and biomedical science perform accurately, safely, reliably, and ethically in the service of better health for all.
In May 2025, the NAM released the special publication, An Artificial Intelligence Code of Conduct for Health and Medicine: Essential Guidance for Aligned Action, which presents a unifying AI Code of Conduct framework developed to align the field around responsible development and application of AI and to catalyze collective action to ensure that the transformative potential of AI in health and medicine is realized.
| 2024-10-23T00:00:00 |
2024/10/23
|
https://nam.edu/our-work/programs/leadership-consortium/health-care-artificial-intelligence-code-of-conduct/
|
[
{
"date": "2022/12/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 40,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 42,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 42,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 43,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 43,
"query": "artificial intelligence healthcare"
}
] |
Artificial Intelligence in Healthcare - Fraunhofer IKS
|
Artificial Intelligence in Healthcare
|
https://www.iks.fraunhofer.de
|
[] |
AI are rapidly changing the healthcare sector. In clinics, hospitals and doctors' offices, electronic health records (EHR), data management systems, AI- ...
|
Digitalization generates huge amounts of data. Industry 4.0 demonstrates how this data can be used effectively. Data is networked to monitor processes, to identify trends at an early stage and to be able to respond to them using new business models. These advantages can also be applied to medicine. While taking into account all the medical and non-medical data, it becomes possible to make efficient and rational decisions, individualize therapies or detect diseases at an early stage.
Big Data and artificial intelligence (AI) are important keywords in the medicine of the future. AI can combine and analyze large amounts of data in a very short time, faster than humans ever could. This paves the way for intelligent applications in the fields of:
Clinical decision-making
Robot-assisted surgery
Medical imaging and diagnostics
Chronic disease monitoring
Hospital Data Management
At Fraunhofer IKS, we conduct research on the validation of digital applications in these safety-critical areas.
| 2022-12-01T00:00:00 |
https://www.iks.fraunhofer.de/en/topics/artificial-intelligence/artificial-intelligence-medicine.html
|
[
{
"date": "2022/12/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 49,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 46,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 46,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 38,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 46,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 44,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 39,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 40,
"query": "AI healthcare"
},
{
"date": "2025/06/01",
"position": 38,
"query": "AI healthcare"
}
] |
|
AI healthcare benefits - IBM
|
AI healthcare benefits
|
https://www.ibm.com
|
[] |
AI is used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals.
|
One benefit the use of AI brings to health systems is making gathering and sharing information easier. AI can help providers keep track of patient data more efficiently.
One example is diabetes. According to the Centers for Disease Control and Prevention, 11.6% of the US population has diabetes. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team.
AI can help providers gather that information, store, and analyze it, and provide data-driven insights from vast numbers of people. Using this information can help healthcare professionals determine how to better treat and manage diseases.
Organizations are also starting to use AI to help improve drug safety. The company SELTA SQUARE, for example, is innovating the pharmacovigilance (PV) process, a legally mandated discipline for detecting and reporting adverse effects from drugs, then assessing, understanding, and preventing those effects.
PV demands significant effort and diligence from pharma producers because it’s performed from the clinical trials phase all the way through the drug’s lifetime availability. Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.
Sometimes, AI might reduce the need to test potential drug compounds physically, which is an enormous cost-savings. High-fidelity molecular simulations can run on computers without incurring the high costs of traditional discovery methods.
AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.
| 2022-12-01T00:00:00 |
https://www.ibm.com/think/insights/ai-healthcare-benefits
|
[
{
"date": "2022/12/01",
"position": 54,
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{
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},
{
"date": "2023/01/01",
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},
{
"date": "2023/02/01",
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{
"date": "2023/02/01",
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},
{
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/07/01",
"position": 15,
"query": "AI healthcare"
},
{
"date": "2023/07/11",
"position": 15,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/08/01",
"position": 15,
"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 18,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 18,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 18,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 53,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 19,
"query": "AI healthcare"
},
{
"date": "2024/05/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 16,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 19,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 18,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 18,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 18,
"query": "AI healthcare"
}
] |
|
Global Initiative on AI for Health
|
Global Initiative on AI for Health
|
https://www.who.int
|
[] |
The GI-AI4H stands as a resilient, long-term institutional structure, grounded in its mission to enable, facilitate, and implement AI in healthcare.
|
In 2018, WHO partnered with the International Telecommunication Union (ITU), setting the stage for the Focus Group on Artificial Intelligence for Health (FG-AI4H), a dynamic platform designed to provide answers to the pressing questions surrounding AI in healthcare.
With increasing global interest and participation in AI advancements, the FG-AI4H recognized the need for a long-term institutional structure. This partnership led to the formation of the Global Initiative on AI for Health (GI-AI4H). Launched in July 2023, under WHO, ITU, and the World Intellectual Property Organization (WIPO), the GI-AI4H stands as a resilient, long-term institutional structure, grounded in its mission to enable, facilitate, and implement AI in healthcare.
| 2022-12-01T00:00:00 |
https://www.who.int/initiatives/global-initiative-on-ai-for-health
|
[
{
"date": "2022/12/01",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/09/01",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 57,
"query": "artificial intelligence healthcare"
}
] |
|
Artificial Intelligence in Health Care | MIT Sloan Executive Education
|
Artificial Intelligence in Health Care
|
https://executive.mit.edu
|
[] |
The Artificial Intelligence in Health Care online short course explores types of AI technology, its applications, limitations, and industry opportunities.
|
In Artificial Intelligence in Health Care, the MIT Sloan School of Management and the MIT J-Clinic aim to equip health care leaders with a grounded understanding of the potential for AI innovations in the health care industry. The course explores types of AI technology, its applications, limitations, and industry opportunities.
The potential of artificial intelligence (AI) to transform health care — through the work of both organizational leaders and medical professionals — is increasingly evident as more real-world clinical applications emerge.
As patient data sets become larger, manual analysis is becoming less feasible. AI has the power to efficiently process data far beyond our own capacity, and has already enabled innovation in areas including chemotherapy regimens, patient care, breast cancer risk, and even ICU death prediction.
With this program, the MIT Sloan School of Management and the MIT J-Clinic aims to equip health care leaders with a grounded understanding of the potential for AI innovations in the health care industry. The Artificial Intelligence in Health Care online short course explores types of AI technology, its applications, limitations, and industry opportunities. Techniques like natural language processing, data analytics, and machine learning will be investigated across contexts such as disease diagnosis and hospital management.
| 2022-12-01T00:00:00 |
https://executive.mit.edu/course/artificial-intelligence-in-health-care/a056g00000URaaTAAT.html
|
[
{
"date": "2022/12/01",
"position": 59,
"query": "artificial intelligence healthcare"
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{
"date": "2023/01/01",
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"query": "artificial intelligence healthcare"
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{
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"query": "artificial intelligence healthcare"
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{
"date": "2023/03/01",
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"query": "artificial intelligence healthcare"
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{
"date": "2023/04/01",
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"query": "artificial intelligence healthcare"
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{
"date": "2023/09/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 50,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 59,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 61,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 62,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 63,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 63,
"query": "artificial intelligence healthcare"
}
] |
|
Exploring the Role of Artificial Intelligence in Smart Healthcare - MDPI
|
Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review
|
https://www.mdpi.com
|
[
"Abbas",
"Syed Raza",
"Seol",
"Lee",
"Seung Won",
"Syed Raza Abbas",
"Huiseung Seol",
"Zeeshan Abbas",
"Seung Won Lee"
] |
Artificial Intelligence (AI) is playing an increasingly pivotal role in advancing smart healthcare systems by enhancing diagnostic accuracy, ...
|
1. Introduction
Artificial Intelligence (AI) is playing an increasingly pivotal role in advancing smart healthcare systems by enhancing diagnostic accuracy, enabling personalized treatment, and improving operational efficiency. For instance, AI models have shown great promise in biomedical signal processing as seen in anesthesia stage classification using near-infrared spectroscopy signals [ 1 ] and in the enhancement of spin-exchange relaxation-free magnetometers for better physiological sensing [ 2 ]. Moreover, sensor calibration improvements like in situ magnetic field compensation for magnetometers [ 3 ] and neuromorphic-enabled cell sorting [ 4 ] further exemplify the integration of AI into medical instrumentation. In the realm of predictive diagnostics, machine learning approaches are being leveraged for the early detection of conditions such as sepsis [ 5 ] and perioperative neurocognitive disorders [ 6 ], while outlier detection models contribute to identifying abnormal clinical data patterns [ 7 ]. Deep learning also supports precise evaluation in diseases like ulcerative colitis through lesion-level analysis [ 8 ] and enhances post-treatment outcomes as demonstrated in AI-assisted nutritional management for cancer patients [ 9 ].
The integration of AI into healthcare systems has led to a new era of smart healthcare, characterized by increased diagnostic precision, personalized treatment recommendations, and streamlined clinical workflows [ 10 11 ]. Smart healthcare refers to the use of advanced technologies, particularly AI, to improve the quality, accessibility, and efficiency of healthcare services while supporting clinicians and empowering patients. AI in this context acts as a transformative force augmenting human intelligence, automating labor intensive processes, and enabling data-driven clinical decisions [ 12 ].
Beyond clinical applications, AI contributes significantly to molecular biology, mental health, and healthcare systems management. In genomics, models like GenoM7GNet [ 13 ] and integrative deep learning approaches for RNA structure prediction [ 14 ] are advancing our understanding of molecular mechanisms. Mental health research benefits from AI-enabled multimodal analysis, helping elucidate the pathways involved in schizophrenia [ 15 ] and postpartum depression [ 16 ]. Furthermore, the optimization of pharmaceutical analysis using AI-enhanced HPLC-MS/MS workflows [ 17 ] and the deployment of AI-driven control systems in teleoperation [ 18 ] illustrate its broader role in healthcare operations. Spatial analytics have also been applied to map the medical device industry in China, aiding strategic health system planning [ 19 ]. Collectively, these developments underscore the multifaceted capabilities of AI in transforming healthcare from molecular diagnostics to large-scale public health and industrial applications.
Over the past decade, the rapid digitization of medical records, wearable technologies, and diagnostic imaging has generated vast amounts of healthcare data. This explosion of data, coupled with advancements in machine learning (ML) algorithms, has significantly accelerated the adoption of AI in clinical environments [ 20 ]. As healthcare systems worldwide grapple with increasing demand, aging populations, and chronic disease burdens, AI offers scalable solutions that can deliver more efficient and equitable care [ 21 22 ].
The current applications of AI in smart healthcare are predominantly classified as Narrow AI systems, task-specific models developed to perform clearly defined functions such as interpreting medical images, detecting anomalies in physiological signals, triaging patients, or assisting in robotic surgeries [ 23 ]. While these systems lack the general reasoning capabilities inherent to humans, they can often surpass clinician performance in specialized domains when trained on high-quality datasets. For instance, deep learning (DL) models have demonstrated expert-level accuracy in diagnosing conditions like pneumonia, diabetic retinopathy, cardiovascular diseases, and breast cancer from medical imaging data [ 24 25 ]. Beyond diagnostics, AI-based virtual assistants such as Wysa and Woebot are proving effective in supporting mental health by delivering around the clock cognitive behavioral therapy and emotional support [ 26 ]. However, despite these advancements, the integration of emerging AI techniques such as federated learning remains in its early stages particularly in South Korea, where pilot projects face substantial interoperability challenges due to the heterogeneity of hospital IT infrastructures [ 27 ].
However, although Narrow AI is very useful, it can only perform specific tasks and cannot adapt to different types of problems. Because of this limitation, researchers are now focusing more on developing AGI, which aims to think and understand like humans, including reasoning, abstract thinking, and recognizing emotions [ 28 ]. Although AGI is still mostly a theoretical concept, it has great potential in healthcare. An AGI system could combine different types of patient information, like doctor’s notes, lab tests, genetic data, and medical images, to provide complete and personalized care advice. However, because of the current limitations in technology, as well as ethical and safety concerns, these systems are not yet used in real medical practice. AGI in healthcare builds upon the capabilities of generative AI by enabling more advanced and adaptive applications such as multimodal patient understanding, where diverse data types like clinical notes, imaging, and genomic data are seamlessly integrated for deeper insights. It supports real-time clinical decision-making by offering dynamic, context-aware recommendations during diagnosis and treatment. These advancements make healthcare more personalized, adaptive, and empathetic [ 29 30 ], expanding the transformative potential shown in Figure 1
In addition to capability-based classification, AI in smart healthcare can be understood through its functional architecture, which defines how the system operates. The simplest functional class, reactive machines, responds only to current inputs and lacks memory of past interactions. These systems have been used in early rule-based alert systems and decision trees but are increasingly being replaced by limited memory systems. Limited Memory AI can analyze historical data to improve predictive accuracy and is widely used in patient monitoring, chronic disease management, and personalized treatment planning [ 31 ].
Advanced functionality types such as Theory of Mind (ToM) and self-aware AI represent ambitious goals for AI researchers. ToM AI would be capable of interpreting patient emotions, intentions, and psychological states, offering a new dimension of empathic and context-aware care [ 32 ]. This is particularly valuable in domains such as mental health, pediatrics, or palliative care, where human factors and emotional hints significantly influence treatment outcomes. Although prototypes exist in controlled environments, no fully operational ToM AI has been deployed clinically due to challenges in data representation, social cognition modeling, and trustworthiness [ 33 ].
Self-aware AI, which would possess consciousness and the ability to self regulate, remains purely speculative. While intriguing for future possibilities like autonomous surgical planning or independent disease research, it raises complex ethical and legal concerns about agency, liability, and control [ 34 ].
The convergence of AI capabilities and functionalities enables increasingly sophisticated healthcare systems. Federated learning is a form of collaborative ML without centralized data collection and has been employed to train AI models across multiple hospitals while preserving patient privacy [ 35 36 ]. Similarly, blockchain technology is being integrated with AI to ensure data transparency, traceability, and security in smart healthcare infrastructures [ 37 ].
In summary, AI is not a monolithic tool but a complex discipline with varying levels of intelligence and operational mechanisms. Understanding AI in healthcare through both capability-based and functionality-based lenses allows for a more evaluation of its current maturity, safety, and future trajectory. As we transition toward increasingly autonomous and intelligent systems, it becomes essential to balance innovation with ethical safeguards, ensuring AI continues to serve the core goal of healthcare improving human well-being.
1.1. Scope This review focuses on the integration of AI into smart healthcare systems, specifically from two distinct yet complementary perspectives: AI capabilities and AI functionalities. The scope encompasses recent advances in AI technologies deployed for diagnosis, monitoring, treatment planning, mental health support, and personalized medicine. It covers real-world applications, ongoing research, and theoretical developments within the healthcare domain, emphasizing AI systems developed and implemented since 2021. The review spans across both clinical care (e.g., imaging, triage, and predictive modeling) and patient-centered applications. Table 1 shows the uniqueness of this review with other reviews.
1.2. Purpose The primary purpose of this review is to provide a structured and comprehensive understanding of how AI is being utilized in smart healthcare, categorized by its levels of intelligence (capabilities) and operational mechanisms (functionalities). Existing reviews often focus on specific technologies or applications without clearly distinguishing between different types of AI maturity or behavior. Figure 2 shows an AGI architecture for smart healthcare. It integrates multimodal inputs, neuro-symbolic reasoning, and emotion fusion to support ethical, explainable, and trustworthy patient care. This paper fills that gap by the following: Clarifying the capability spectrum from narrow to Superintelligent AI.
Outlining the functional evolution from reactive machines to theoretical self-aware systems.
Mapping current technologies to these categories to evaluate readiness, risk, and research opportunities. This framework helps healthcare professionals, AI developers, researchers, and policymakers better understand what AI can do today, what it may be capable of tomorrow, and how to design and regulate its use responsibly.
| 2025-01-14T00:00:00 |
2025/01/14
|
https://www.mdpi.com/2227-9032/13/14/1642
|
[
{
"date": "2022/12/01",
"position": 63,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 70,
"query": "AI healthcare"
},
{
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{
"date": "2023/02/01",
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{
"date": "2024/01/01",
"position": 83,
"query": "AI healthcare"
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{
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{
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{
"date": "2024/06/01",
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},
{
"date": "2024/09/01",
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"query": "AI healthcare"
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{
"date": "2024/10/01",
"position": 69,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 79,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 70,
"query": "artificial intelligence healthcare"
}
] |
The 2025 AI Index Report | Stanford HAI
|
The 2025 AI Index Report
|
https://hai.stanford.edu
|
[] |
AI is increasingly embedded in everyday life. From healthcare to transportation, AI is rapidly moving from the lab to daily life. In 2023, the FDA approved ...
|
9. AI and computer science education is expanding—but gaps in access and readiness persist.
Two-thirds of countries now offer or plan to offer K–12 CS education—twice as many as in 2019—with Africa and Latin America making the most progress. In the U.S., the number of graduates with bachelor’s degrees in computing has increased 22% over the last 10 years. Yet access remains limited in many African countries due to basic infrastructure gaps like electricity. In the U.S., 81% of K–12 CS teachers say AI should be part of foundational CS education, but less than half feel equipped to teach it.
| 2022-12-01T00:00:00 |
https://hai.stanford.edu/ai-index/2025-ai-index-report
|
[
{
"date": "2022/12/01",
"position": 65,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 91,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 98,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 89,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 91,
"query": "AI skills gap"
},
{
"date": "2023/03/01",
"position": 91,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 84,
"query": "artificial intelligence healthcare"
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},
{
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"position": 92,
"query": "AI skills gap"
},
{
"date": "2023/07/01",
"position": 97,
"query": "AI impact jobs"
},
{
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"position": 92,
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},
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},
{
"date": "2023/12/01",
"position": 86,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 99,
"query": "AI impact jobs"
},
{
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{
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},
{
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"position": 99,
"query": "AI impact jobs"
},
{
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},
{
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},
{
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},
{
"date": "2024/11/01",
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"query": "AI impact jobs"
},
{
"date": "2025/01/01",
"position": 99,
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},
{
"date": "2025/01/01",
"position": 84,
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}
] |
|
Heidi Health - Best AI Medical Scribe for All Clinicians
|
Best AI Medical Scribe for All Clinicians
|
https://www.heidihealth.com
|
[
"Lisa Terwilliger",
"Corey Dickinson",
"Christopher Rodriguez",
"Shelbie Scharf"
] |
Heidi is the ambient AI medical scribe that automates clinical documentation to reduce administrative burden and enable healthcare professionals to focus more ...
|
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| 2022-12-01T00:00:00 |
https://www.heidihealth.com/
|
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{
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{
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},
{
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}
] |
|
“Skynet is coming!” – should we trust AI in healthcare?
|
“Skynet is coming!” – should we trust AI in healthcare?
|
https://www.bridgeheadsoftware.com
|
[
"John Mccann",
"Bridgehead Software"
] |
In his new blog, BridgeHead Principal Solutions Consultant, Bobby Edwards, explores whether we can and should trust AI in healthcare.
|
“The Skynet is Coming! The Skynet is Coming!”
[To get the full effect of this story, you have to imagine the following narration in Chicken Little’s voice]
Oh dear! Oh dear! Have you heard the latest, friends? The machines are coming! The algorithms are learning! The Skynet is Coming and medicine will never be the same!
It all started with a humble chatbot, you see. Just a few polite words of advice here and there. “What symptoms do you have?” it asked sweetly. “Let me tell you what Dr. Watson has to say about that,” it chirped. But now – oh, now – there are AI algorithms reading X-rays faster than radiologists, predicting strokes before they happen, and (some say) planning to take over the entire healthcare system!
Doctors beware! Nurses, hide your charts!
The AI is coming to steal your job, your stethoscope, and perhaps even your coffee break! They call it “machine learning,” but I call it “Skynet rising!” They say these AIs can see patterns we mortals can’t, but what if they see too much? What if they start diagnosing everything as “Patient: Terminated!”?
Oh, the marvels of these robot doctors! They can scan gigabytes of data in seconds, spot a malignant mole from a mile away, and even predict when the next flu season will hit. But what about compassion? What about the warm bedside manner of Patch Adams? Will these AI bots offer a reassuring pat on the shoulder when you’re nervous about your colonoscopy?
The Skynet is coming! And it’s falling hard!
Every week, there’s a new study showing how AI is better at this or faster at that. They promise to reduce errors and make healthcare more efficient – like an army of tireless nurses who never call in sick. But I can’t help but squawk and flutter. After all, wasn’t it the promise of “efficiency” that first gave us Skynet in the movies?
Will the sky actually fall?
Well, maybe not. Maybe, just maybe, these algorithms will become our allies, not our overlords. They’ll handle the boring stuff – sorting images, analyzing data, and alerting doctors to subtle signals – while we humans do what we do best: caring, comforting, and connecting.
But I’m still watching the sky, friends. I’m still listening for the whir of the robot’s gears in the hallway. Because today it’s AI-assisted charting, tomorrow it’s AI-powered diagnosis, and the day after? Who knows – maybe it will be Skynet.
Stay alert! Stay vigilant! And above all – don’t let the robots take your stethoscope without a fight.”
I thought ChatGPT did a pretty good job, right?
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.bridgeheadsoftware.com/2025/07/skynet-is-coming-should-we-trust-ai-in-healthcare/
|
[
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},
{
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},
{
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},
{
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},
{
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},
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},
{
"date": "2025/06/01",
"position": 85,
"query": "AI healthcare"
}
] |
An Example AI Readiness in Healthcare Assessment Framework
|
An Example AI Readiness in Healthcare Assessment Framework
|
https://solutionsreview.com
|
[
"Recent Posts"
] |
Artificial intelligence is reshaping modern healthcare. From AI-powered diagnostic tools and predictive triage systems to personalized treatment ...
|
Tim King offers an example AI readiness in healthcare assessment framework, part of Solutions Review’s coverage on the human impact of AI.
Artificial intelligence is reshaping modern healthcare. From AI-powered diagnostic tools and predictive triage systems to personalized treatment planning and hospital operations optimization, the promises are profound—faster care, fewer errors, better outcomes. But for every headline about AI revolutionizing medicine, there are urgent and unanswered questions: Can we trust the outputs? Are they fair? Is patient privacy truly protected? And who is accountable when things go wrong?
The Imperative of AI Readiness in Healthcare
In a sector where human lives are on the line, innovation alone is not enough. AI in healthcare must be not only powerful—it must be responsible, ethical, and deeply human-centered. And that’s where AI readiness comes in.
AI readiness is the capacity of a healthcare organization to adopt artificial intelligence in ways that are clinically safe, ethically sound, legally compliant, and culturally sustainable. It means aligning your data, people, processes, and oversight structures before a single model is deployed. It’s the difference between using AI as a flashy add-on and building it as a trusted clinical asset.
Unlike other industries, healthcare faces a uniquely complex AI landscape:
Regulatory sensitivity , including FDA oversight for AI-as-medical-device applications
Privacy imperatives , governed by HIPAA, GDPR, and evolving patient consent standards
High-stakes use cases , where AI is involved in diagnosing, treating, or triaging care
Equity risks , as algorithmic bias can exacerbate health disparities across race, gender, and socioeconomic status
Workflow pressure, where AI tools must enhance—not disrupt—the clinician’s ability to care
This framework exists to help you prepare for that landscape. It delivers a full-spectrum view of what healthcare organizations must consider to become AI-ready—from data integration and model validation to ethical patient-facing tools, bias mitigation, workforce training, and vendor governance.
Whether you’re a hospital system, a payer network, a digital health startup, or a national health agency, AI readiness is your next patient safety initiative—and your next competitive edge.
AI Readiness in Healthcare Assessment Framework
Data Foundations: Quality, Integration & Interoperability
AI is only as good as the data it learns from—and in healthcare, that data is notoriously fragmented, inconsistent, and locked in silos. From EHRs and lab systems to imaging archives and patient wearables, clinical data often lives in disconnected formats, behind incompatible firewalls, and buried in unstructured notes. That’s why the first step toward AI readiness in healthcare isn’t about the algorithm. It’s about the data.
A truly AI-ready healthcare organization ensures that its data ecosystem is:
High-quality and error-checked
Inconsistent coding, missing values, or outdated records can severely skew AI models. Readiness includes data profiling, quality assurance protocols, and automated error detection systems.
De-siloed and integrated
Vital patient information must flow across departments and systems—labs, imaging, pharmacy, admissions, and primary care. AI readiness demands APIs, ETL pipelines, or data fabrics that bridge these sources while respecting access controls.
Standards-based and interoperable
AI needs structured, labeled, and machine-readable data to function effectively. Using HL7 FHIR, SNOMED CT, LOINC, and ICD coding schemes not only supports model training but also improves model portability across systems.
Real-time or near-real-time
Many AI tools—such as early sepsis detection or emergency department triage—require live data feeds. Static, batch-mode data limits the utility of AI in time-sensitive clinical settings.
Inclusive of patient-generated data
Increasingly, wearables, mobile apps, and remote monitoring tools are generating health insights outside the hospital walls. AI readiness includes a governance strategy for how this data is validated, integrated, and used in clinical AI tools.
Ethically sourced and consent-aligned
Data used for training, testing, and deploying AI must comply with HIPAA, GDPR, and informed consent principles. Readiness includes maintaining clear data provenance, usage logs, and patient opt-out pathways.
Failing to address data quality and interoperability doesn’t just slow down AI—it endangers patients. A model trained on biased, incomplete, or siloed data may misdiagnose, misallocate resources, or worsen disparities. But when data foundations are strong, AI becomes a powerful clinical ally—capable of spotting patterns, surfacing risks, and supporting decisions with confidence.
Clinical Decision Support Systems & Diagnostic AI
Few applications of AI are more promising—or more perilous—than those involved in diagnosis and treatment planning. From imaging interpretation to sepsis alerts, AI-enabled Clinical Decision Support Systems (CDSS) are increasingly being embedded into physician workflows. But as they influence decisions with life-altering consequences, these systems must meet the highest possible standard of validation, safety, explainability, and clinical alignment.
AI readiness in clinical decision support isn’t just about model accuracy—it’s about integration, trust, and responsibility.
To be ready for safe and effective CDS deployment, healthcare organizations must ensure:
Rigorous Pre-Deployment Validation
Models used in diagnosis or treatment decisions must undergo validation using real-world data from the patient population they’ll serve. External validation, peer review, and performance audits against gold-standard datasets are essential steps before go-live.
Defined Clinical Use Cases and Limitations
AI tools should be explicitly scoped. Is the tool designed to suggest differential diagnoses? Predict deterioration? Recommend dosing adjustments? Readiness includes clearly documented indications, contraindications, and boundaries to avoid misuse.
Clinician-in-the-Loop Design
CDS should support—not replace—clinician judgment. Systems must be designed to enhance trust: showing probability scores, confidence levels, and the rationale behind outputs. A “black box” is not an acceptable clinical partner.
Workflow Alignment & Usability
If AI alerts are too frequent, too vague, or poorly timed, clinicians will ignore them. Readiness includes human-centered design and field testing to ensure AI fits seamlessly into chart review, patient rounds, or consult workflows.
Fail-Safe & Override Protocols
Clinicians must have the ability to override AI recommendations—and that action should trigger learning and quality review, not punishment. Readiness includes building protocols for override logging, feedback loops, and escalation if AI errors occur.
Post-Deployment Monitoring
AI tools may “drift” as patient populations, clinical guidelines, or hospital practices change. Regular performance monitoring and recalibration are necessary to ensure models continue to meet safety and efficacy standards.
The promise of CDS and diagnostic AI is enormous: faster detection of critical illness, more precise treatment choices, reduced variation in care. But without readiness, these tools can add cognitive burden, generate alert fatigue, or, in the worst cases, cause patient harm. When governed responsibly, they represent the best of human-machine collaboration in medicine.
Patient Privacy, Consent & Data Ethics
In healthcare, privacy is sacred. Every AI deployment must uphold the same standard of confidentiality and ethical stewardship that clinicians have honored for generations. But AI introduces new complexity. From massive datasets used for model training to real-time decision support embedded in care delivery, it’s no longer enough to check the HIPAA box. AI readiness demands a deeper commitment to patient privacy, informed consent, and ethical data use.
Healthcare organizations that want to responsibly integrate AI must prepare to navigate:
Data Minimization & De-Identification
AI doesn’t need access to every patient detail. Readiness means applying the principles of data minimization—using only the data necessary for a specific model task—and de-identifying datasets where possible without compromising model utility.
Risk of Re-Identification
With powerful AI tools, even de-identified data can sometimes be reverse-engineered—especially when combined with external sources. Organizations must assess and monitor re-identification risk as a continuous threat vector, not a one-time audit.
Transparent, Layered Consent Models
Traditional consent forms don’t cover the complexities of AI. Readiness includes implementing layered, dynamic consent that informs patients not just about data use in care, but how their data may be used in model training, algorithm improvement, and third-party partnerships.
Ethical Use of Non-Clinical Data
AI systems are increasingly trained on lifestyle, behavioral, and social determinants of health (SDOH) data—sometimes acquired from third parties or digital tools. Organizations must have governance protocols that vet these sources for ethical integrity and patient awareness.
Right to Explanation & Opt-Out
Patients should have the right to understand when AI has influenced their care and to opt out where feasible. This builds trust and aligns with growing legal precedents around AI transparency and algorithmic accountability.
Data Use Governance Boards
Just as IRBs govern human subject research, healthcare organizations should establish AI Data Use Boards to review how data is acquired, shared, used in training, and linked across systems. These boards act as both oversight and ethical compass.
Failing to prepare for these challenges can erode trust, trigger compliance violations, and risk reputational damage. But when handled well, ethical data practices become a cornerstone of AI trust—building bridges between innovation and patient dignity.
Bias, Fairness & Equity in AI Healthcare Systems
Healthcare is already burdened by disparities—across race, gender, income, geography, and more. When AI enters the picture, it has the power to either magnify those inequities or help correct them. Which direction it takes depends entirely on how systems are designed, trained, and governed. That’s why bias mitigation and fairness aren’t optional features of AI in healthcare—they are foundational requirements for readiness.
Many AI systems unintentionally encode and reproduce historical inequities. If a model is trained on datasets that underrepresent certain populations or reflect biased clinical patterns, it may deliver inaccurate, delayed, or harmful outputs for vulnerable groups. Readiness means proactively rooting out those risks at every step of the AI lifecycle.
Key components of equity-focused AI readiness in healthcare include:
Bias Audits at the Model Level
All clinical and operational AI systems should undergo demographic performance analysis. Are accuracy, sensitivity, and specificity consistent across racial, ethnic, gender, age, and language subgroups? Disparities must be identified, remediated, and continuously monitored.
Bias Awareness in Upstream Data
Even before training, readiness means assessing whether the data itself is biased. Are certain patient groups underrepresented due to systemic barriers, historical mistrust, or geographic isolation? If so, the model may fail them—regardless of architecture.
Fairness by Design Practices
AI developers should embed fairness constraints, resampling techniques, or post-processing corrections directly into model development. This helps ensure equitable performance isn’t an afterthought—but a guiding objective.
Inclusion of Affected Populations in Design & Review
If an AI tool will be used to predict outcomes in Black patients, elderly populations, or non-English speakers, representatives of those groups should help shape its design, testing, and rollout. Lived experience enhances not only ethics—but effectiveness.
Impact Monitoring Over Time
AI models may evolve—or their environments may change. Readiness includes ongoing fairness evaluation, especially when tools are updated, retrained, or deployed in new populations.
Transparency & Disclosure of Limitations
If a model is known to underperform in a specific subgroup, that information should be disclosed to clinicians and decision-makers. Readiness includes policies for flagging known limitations and guiding safer use.
Equity in healthcare is not just a social goal—it’s a clinical necessity. When AI systems perform poorly for underserved populations, the result isn’t just unfair—it’s unsafe. But with the right oversight, inclusive design, and intentional audits, healthcare AI can be a powerful force for narrowing gaps, not widening them.
Workforce AI Literacy & Clinical Integration
Even the most sophisticated AI tool is only as effective as the people using it. In healthcare, that means physicians, nurses, administrators, IT leaders, and support staff must all understand—not just how to operate AI-enabled systems—but how to interpret them, question them, and govern them. AI literacy and integration into clinical workflows are mission-critical to realizing the promise of responsible healthcare AI.
Yet today, many frontline professionals are unsure what AI can and can’t do. Some may blindly trust outputs they don’t fully understand. Others may resist using AI altogether, fearing it could replace their judgment or create legal exposure. And when AI tools are bolted onto legacy systems without regard for clinical flow, they add friction—not value.
True readiness requires a people-first approach that empowers healthcare workers to become active participants—not passive recipients—in the AI era.
Key components of workforce AI readiness in healthcare include:
Role-Specific AI Literacy Training
Not every clinician needs to understand backpropagation or neural network tuning. But they do need to know what a prediction score means, what biases might be present, and when to trust or challenge a model. Training should be tailored to roles, with practical, case-based examples.
Co-Design with Clinical Stakeholders
AI solutions should never be developed in a vacuum. Involving physicians, nurses, pharmacists, and care coordinators in the design process helps ensure tools are usable, trustworthy, and aligned with real-world needs.
Integrated Clinical Workflows
AI outputs must surface at the right moment, in the right format, within existing EHR or clinical systems. Pop-up alerts, dashboards, and visualizations should minimize disruption and maximize decision support. Poor integration undermines adoption.
Change Management & Cultural Readiness
AI adoption is not just a technical shift—it’s a cultural one. Leadership must foster an environment where asking questions about AI, reporting concerns, and suggesting improvements are encouraged—not penalized. Transparency builds confidence.
Cross-Functional AI Champions
Identify and train internal champions—clinicians, data scientists, informatics leads—who can bridge communication gaps and model responsible AI usage. Champions help normalize adoption and serve as the connective tissue of AI transformation.
Workforce Metrics & Feedback Loops
Readiness includes monitoring how staff use and perceive AI tools. Are they helpful? Are they trusted? Are they adding value or causing stress? Ongoing feedback informs both system design and training needs.
When clinicians understand AI’s role and feel confident using it, adoption increases, outcomes improve, and safety is preserved. When they’re left out or overwhelmed, even the best algorithms sit unused—or worse, misused. A truly AI-ready healthcare system invests in its people first.
AI Governance, Oversight & Regulatory Alignment
In healthcare, no new drug reaches patients without rigorous trials and regulatory approval. The same must be true for artificial intelligence. As AI systems become clinical instruments—impacting diagnoses, treatment pathways, and patient communication—they require structured governance, ethical oversight, and regulatory compliance at every step.
AI readiness means moving beyond innovation theater into sustained, accountable deployment. Healthcare organizations must treat AI not just as a tool, but as a governed clinical asset—with rules, reviews, and responsibilities that mirror those applied to any other intervention.
To achieve this, a comprehensive AI governance structure in healthcare should include:
Formal AI Governance Board or Council
A multidisciplinary oversight body—comprising clinicians, ethicists, data scientists, compliance officers, and patient advocates—should review and approve AI systems before and after deployment. The board’s role is not to stifle innovation, but to safeguard safety, fairness, and transparency.
Defined Approval Workflows
AI models and tools should follow standardized pipelines for review: including technical validation, ethical risk assessment, regulatory alignment, and post-deployment monitoring protocols. Ad hoc deployments pose unacceptable risk in healthcare.
Compliance with Regulatory Frameworks (e.g., FDA SaMD)
AI models that qualify as Software as a Medical Device (SaMD) must align with FDA (or global equivalent) guidance. Readiness includes maintaining robust documentation, version control, and submission-ready evidence for intended use and safety performance.
Deployment Ethics Files (DEFs)
Each AI system should have a living file documenting its purpose, assumptions, training data, risks, mitigation strategies, and human oversight plan. These files enhance internal accountability and create an audit trail for regulators or litigators.
Human Oversight Designation
Every AI system should have a named individual or team responsible for monitoring use, fielding concerns, and coordinating updates. In the age of distributed automation, governance must remain anchored in human responsibility.
Incident Review & Escalation Policies
Just as hospitals have morbidity and mortality rounds, AI-related errors, false positives/negatives, or unintended outcomes should be tracked and reviewed. Governance readiness includes red flag escalation channels and corrective action protocols.
Public-Facing Transparency Statements
Patients and clinicians alike should be able to see what AI tools are in use, what decisions they influence, and how they are governed. Transparency builds trust, and trust accelerates safe adoption.
Without governance, AI in healthcare becomes a liability—technically potent but ethically fragile. But with the right structures in place, it becomes a durable asset: responsive, reviewable, and respected by clinicians, regulators, and the public alike.
Patient-Facing AI Tools & Digital Health Applications
From symptom checkers and chatbots to AI-powered wellness apps and remote monitoring tools, artificial intelligence is no longer confined to the clinic or hospital—it’s in patients’ hands. While these tools promise accessibility, efficiency, and personalization, they also raise serious concerns about safety, trust, and misinformation. AI readiness in healthcare must extend beyond the walls of the institution to include the digital front door.
When patients interact directly with AI, the risks and responsibilities shift. Unlike clinicians, patients may not know when they’re receiving AI-generated advice. They may not question its accuracy. They may not have an immediate way to escalate confusion or concern. For AI to be a trusted partner in digital health, readiness requires both technical rigor and human-centered design.
Key readiness considerations for patient-facing AI tools include:
Transparent Disclosure of AI Use
Patients should know when they are interacting with AI—whether it’s a symptom checker, appointment scheduler, or post-op care chatbot. Clarity builds trust and sets appropriate expectations.
Plain-Language Communication
AI-generated outputs must be delivered in language that is easy to understand across health literacy levels. Medical jargon, vague risk scores, or non-actionable guidance erode usability and safety.
Escalation Pathways to Human Care
Every AI-driven interaction should offer a clear path to human support. Whether it’s a nurse hotline, appointment scheduler, or emergency prompt, escalation ensures that patients aren’t left navigating uncertainty alone.
Guardrails for Medical Advice & Misinformation
Patient-facing AI must be strictly scoped. It should never offer a diagnosis, prescribe medication, or override clinical advice unless it is FDA-cleared and supervised. Content moderation and clinical accuracy protocols must be embedded from the start.
Data Security & Consent for Digital Interactions
Wearables, mobile health apps, and browser-based tools all collect sensitive information. Readiness includes securing patient data, limiting unnecessary collection, and obtaining clear consent—especially when sharing with third parties or integrating into EHRs.
Monitoring & Continuous Improvement
Usage patterns, drop-off rates, and flagged complaints should be monitored in real time. Feedback loops allow teams to refine content, clarify confusing responses, and improve experience over time.
Patient-facing AI has enormous potential to increase access, support self-care, and personalize engagement—but only if it’s designed with empathy and guardrails. When readiness is overlooked, these tools become a new vector for harm, inequity, or confusion. But when readiness is prioritized, they become an extension of trusted care—available 24/7, responsive to patient needs, and always backed by human oversight.
Hospital Operations, Admin & Financial Optimization
While clinical decision support and patient engagement often steal the spotlight, some of the most immediate and scalable AI gains in healthcare come from behind the scenes. Scheduling optimization, billing accuracy, staffing predictions, and supply chain automation—these are areas where AI can quietly drive efficiency, reduce costs, and ease administrative burdens.
But just because these systems aren’t directly patient-facing doesn’t mean they’re risk-free. When AI governs who gets an appointment, how a claim is coded, or whether a case is flagged for audit, it’s making decisions with real consequences for access, equity, and revenue. Readiness in these domains is about ensuring AI enhances—not erodes—fairness, transparency, and operational integrity.
To prepare for AI in hospital operations and administrative optimization, organizations must:
Audit AI for Equity & Access
Does your scheduling model inadvertently deprioritize patients from certain ZIP codes? Does your billing AI flag claims from specific populations more frequently? Readiness means proactively testing for bias in administrative algorithms.
Validate Financial Models for Accuracy & Interpretability
Revenue cycle AI tools that optimize reimbursement or predict denials must be interpretable by finance teams and compliant with payer rules. Black-box systems can create friction with insurers and expose hospitals to audits or penalties.
Align Staffing & Capacity Models with Human Oversight
AI systems that predict ER volume or recommend shift coverage must integrate with HR workflows and clinical judgment. Readiness includes override capabilities and scenario planning—especially in high-stress environments like flu season or pandemics.
Secure Sensitive Operational Data
These tools often rely on protected financial and workforce data. Readiness means encrypting data at rest and in transit, applying least-privilege access models, and documenting how and where operational data is used in model training.
Disclose Automation Use in Patient Communications
If AI is involved in sending reminders, generating billing statements, or handling patient service chat, that automation should be disclosed—and fallbacks to human support must be available.
Monitor for Automation Drift & Overreach
AI that starts by automating billing suggestions can eventually expand into more sensitive tasks if left unchecked. Readiness includes governance controls to manage scope creep and ensure automation stays within intended bounds.
When implemented responsibly, AI in hospital operations can increase throughput, reduce administrative waste, and help staff spend more time on care—not paperwork. But poor implementation risks depersonalized care, opaque billing decisions, and unintentional discrimination.
Post-Deployment Monitoring & Incident Management
AI implementation doesn’t end at deployment—it begins there. In healthcare, where patient lives are at stake, continuous monitoring is not a luxury—it’s a mandate. Models drift. Populations change. Clinical protocols evolve. And without post-deployment vigilance, an AI system that was safe and effective yesterday could become biased, brittle, or dangerous tomorrow.
That’s why a healthcare organization’s AI readiness must include robust protocols for real-time surveillance, incident detection, and ethical responsiveness.
To ensure safe, responsible AI usage over time, organizations must prepare to:
Continuously Monitor Model Performance in Live Environments
Track key indicators such as prediction accuracy, false positives/negatives, clinician override rates, and subgroup performance. Monitoring should be proactive, not just reactive to complaints.
Detect Drift and Trigger Retraining
Over time, input data distributions may change due to new patient demographics, updated clinical standards, or seasonal patterns. Readiness includes having automated alerts and predefined thresholds for when retraining is needed.
Enable Real-Time Flagging of Anomalies or Errors
Clinicians and staff should have a clear, user-friendly method to report concerning outputs—such as inexplicable recommendations or repeated false alarms. These reports must feed into a central triage and response system.
Establish Ethical Incident Response Protocols
Just as hospitals have systems to review adverse drug reactions or medical errors, AI incidents—ranging from patient harm to detected bias—must be logged, investigated, and addressed with transparency.
Track Time-to-Resolution & Remediation
Metrics matter. How long does it take to investigate an AI-related incident? What percentage lead to model changes, workflow updates, or retraining? Readiness includes measuring your capacity to act on insights—not just collect them.
Maintain a “Living” Deployment Ethics File
Every live AI system should have a Deployment Ethics File (DEF) that gets updated over time—capturing monitoring data, incidents, retraining history, and lessons learned. This file serves as a single source of truth for auditors, regulators, and internal stakeholders.
Inform Affected Stakeholders of Material Changes
When an AI system is significantly modified due to performance or risk issues, users—both clinical and administrative—must be notified. Readiness includes structured change communication protocols to prevent confusion or misuse.
Post-deployment monitoring is what separates experimental pilots from enterprise-grade clinical systems. In healthcare, it’s the safety net that ensures AI doesn’t just work when it’s new—it keeps working when it matters most. Without it, blind trust replaces informed oversight. With it, AI becomes a durable and ethical component of care delivery.
Building a Resilient, Responsible AI Future in Healthcare
Artificial intelligence is not a passing trend in healthcare—it is a permanent transformation. From accelerating diagnoses to optimizing operations, AI has the power to enhance every layer of the care continuum. But this power comes with profound responsibility. In no other sector do the consequences of misused or misunderstood AI carry such gravity—because in healthcare, mistakes aren’t just costly; they’re life-altering.
AI readiness is not about chasing the latest technology. It’s about building the trust infrastructure required to use it wisely.
That means preparing your data to be fair, clean, and interoperable. It means validating models with the same scrutiny you’d apply to medical devices. It means investing in your people—so they can partner with AI rather than fear or blindly follow it. And it means establishing governance systems that put human oversight, ethical clarity, and continuous improvement at the core of every deployment.
This framework has walked through every facet of what true AI readiness requires in the healthcare context:
From foundational data quality to bias audits
From patient consent to post-deployment surveillance
From staff training to stakeholder trust
From ethical board reviews to operational ROI
Every section of this article pairs with a practical tool—each one designed to move your team from theory to action. These tools serve as readiness accelerators, empowering your clinical, technical, and executive teams to work together on a shared roadmap toward responsible AI implementation.
The future of healthcare isn’t just high-tech—it’s high-trust.
Whether you’re a hospital CIO, a digital health innovator, a public health agency, or a frontline provider, your AI readiness today will define your ability to deliver compassionate, equitable, and excellent care tomorrow.
Note: These insights were informed through web research and generative AI tools. Solutions Review editors use a multi-prompt approach and model overlay to optimize content for relevance and utility.
| 2025-07-03T00:00:00 |
2025/07/03
|
https://solutionsreview.com/an-example-ai-readiness-in-healthcare-assessment-framework/
|
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Addressing Information Asymmetry in Healthcare Through AI
|
Journal of Healthcare Management
|
https://journals.lww.com
|
[
"Cooley",
"Author Information"
] |
Current AI applications in healthcare (Mathews, 2024) primarily follow two distinct paths: mental health and well-being services, and provider- ...
|
For more information, contact [email protected].
Mr. Cooley, a healthcare administration student at the University of Maryland Global Campus, is the first-place winner in the undergraduate division of the 2025 ACHE Richard J. Stull Student Essay Competition in Healthcare Management. For more information about this competition, contact Sheila T. Brown at (312) 424-9316.
The author declares no conflicts of interest.
| 2025-07-04T00:00:00 |
2025/07/04
|
https://journals.lww.com/jhmonline/fulltext/2025/07000/addressing_information_asymmetry_in_healthcare.5.aspx
|
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7 Best AI in Healthcare Courses (July 2025) - Unite.AI
|
7 Best AI in Healthcare Courses (July 2025)
|
https://www.unite.ai
|
[
"Alex Mcfarland"
] |
artificial intelligence is transforming healthcare like no other industry, driving innovations from diagnostics to hospital operations. in fact, ...
|
Artificial Intelligence is transforming healthcare like no other industry, driving innovations from diagnostics to hospital operations. In fact, 80% of hospitals now use AI to enhance patient care and efficiency. The healthcare AI market is booming – growing from $32 billion in 2024 to a projected $431 billion by 2032. With this surge comes a demand for professionals who understand AI’s applications in medicine. Enrolling in a quality AI in healthcare course can equip you with the skills to leverage AI for better patient outcomes and workflow improvements.
Below, we’ve compiled the best AI in healthcare courses, each with an overview, pros and cons, and pricing details.
Comparison Table of Best AI in Healthcare Courses
Course Best For Price Key Features MIT Sloan (GetSmarter) Healthcare leaders & execs $3,250 No coding, strategic focus, real case studies, MIT certificate Stanford (Coursera) Beginners & cross-functional teams $49/mo 5-course series, patient journey capstone, audit free, Stanford faculty MIT xPRO Engineers & technical professionals $2,650 Neural networks, NLP, AI design, Python projects, CEUs included Harvard Med School Healthcare execs & strategists $3,050 Capstone project, ethics focus, live sessions, high-level strategy Udacity Nanodegree ML engineers & data scientists $399/mo Medical imaging projects, FDA plan writing, mentor support, 4 real-world projects UIUC Certificate Clinicians & non-technical staff $750 CME credits, 6 modules, quick format, certificate from UIUC Johns Hopkins Clinical leaders & program managers $2,990 Predictive analytics, implementation playbook, faculty-led, live masterclasses
MIT Sloan Artificial Intelligence in Health Care Online Short Course | Trailer
Watch this video on YouTube
This is a 6-week online executive course from MIT Sloan School of Management and MIT’s J-Clinic, delivered via GetSmarter. It’s designed to give healthcare leaders a grounded understanding of AI’s potential in healthcare organizations. The curriculum covers the types of AI technologies, their applications, limitations, and industry opportunities.
Participants explore how methods like natural language processing (NLP), data analytics, and machine learning can be applied to contexts such as disease diagnosis and hospital management. Real-world examples (from optimizing chemotherapy regimens to predicting ICU outcomes) illustrate AI’s impact on care. Learners engage through video lectures, case studies, and discussions, and upon completion receive a certificate from MIT Sloan Executive Education.
Pros and Cons MIT Sloan certificate adds credibility
No coding required for learners
Broad coverage of healthcare AI High price for short program
Strategic, not technical, depth
Fast-paced; time-intensive weekly demands
Pricing
3,250 USD for the 6-week program. This includes all materials and the MIT Sloan certificate. No academic credit is given, but the credibility of MIT and the executive education experience are the draw.
Visit MIT Course →
Stanford Med LIVE: The State of AI in Healthcare and Medicine
Watch this video on YouTube
Offered by Stanford University via Coursera, this is a 5-course online specialization exploring how AI can safely and ethically be brought into clinical practice. It covers current and future applications of AI in healthcare, including how machine learning improves patient safety, quality of care, and medical research.
The program is beginner-friendly (no prior experience required) and is designed to bridge healthcare and computer science professionals. Students learn about healthcare data, clinical data analysis, machine learning fundamentals, and evaluating AI tools, culminating in a hands-on capstone project following a patient’s journey through data.
The specialization is highly rated (≈4.7 out of 5) with thousands of learners, reflecting strong content and instructorship. Upon completion, learners earn a shareable certificate from Stanford Medicine.
Pros and Cons Created by Stanford experts
Great for beginners, no coding
Self-paced, modular learning design Lacks instructor interaction
Requires strong self-discipline
Minimal hands-on coding exposure
Pricing
Coursera subscription model (approximately $49 USD/month). The full specialization can be completed in about 1–3 months at ~10 hours/week, making the total cost roughly $50–$150 for most learners. Auditing is free (no certificate), and Coursera often offers 7-day free trials and financial aid for those who qualify.
Visit Stanford Course →
Information session on MIT xPRO’s Artificial Intelligence in Healthcare program
Watch this video on YouTube
MIT xPRO’s online professional program is a 7-week course (5–7 hours/week) focusing on the application of AI in modern healthcare. Co-developed with Emeritus, it dives into technical concepts and their real-world uses. The course assumes some technical background – prior knowledge of calculus, statistics, and basic Python is recommended. Topics include the AI design process (a framework to develop AI solutions), machine learning algorithms and neural networks, natural language processing, and even emerging areas like biomechatronics.
Learners practice applying AI to healthcare problems: for example, using the design process to solve a clinical challenge, running a simple neural network in Python, and ideating an “ingestible robot” for healthcare. The program is project-based and interactive, with insights from MIT faculty and industry experts.
Graduates earn a certificate and 3.5 Continuing Education Units (CEUs) from MIT xPRO, signaling mastery of cutting-edge healthcare AI concepts.
Pros and Cons Strong technical and design focus
Project-based learning with coding
Awarded CEUs from MIT xPRO Requires STEM and Python knowledge
Expensive for a short course
Cohort format limits flexibility
Pricing
$2,650 USD for the 7-week program. This includes course access and support. Employer sponsorship is often encouraged due to the program’s professional development nature. (Note: Admissions are open to professionals worldwide, and installments or financing options may be available through Emeritus.)
Visit MITxPRO Course →
Information session on Harvard Medical School's AI in Health Care: From Strategies to Implementation
Watch this video on YouTube
Offered by Harvard Medical School’s Executive Education division, this is an 8-week online course for healthcare leaders and decision-makers. It aims to equip participants to design, pitch, and implement AI-driven solutions in healthcare settings. The curriculum blends theory with practice: participants learn to evaluate current AI systems, identify opportunities for AI in their organizations, assess ethical and regulatory implications, and develop a strategic roadmap for adoption.
A hallmark is the capstone project where learners must propose an AI solution for a real healthcare challenge, applying concepts from each module to plan its implementation. The program is instructor-paced with weekly video lectures by Harvard faculty, live webinar sessions, and peer discussion forums. Graduates receive a digital Certificate of Completion from Harvard Medical School, and gain exposure to an elite network of healthcare professionals working on AI.
Pros and Cons Taught by Harvard faculty
Strategic and implementation-focused
Includes live sessions and capstone Premium tuition pricing
No technical coding content
Fixed schedule, less flexibility
Pricing
$3,050 USD for the 8-week program. The fee includes all course materials and access to Harvard’s online platform. Discounts may be available for groups or early registration. Given the high caliber of the program, many participants have their employers cover the tuition as an investment in innovation skills.
Visit Harvard Course →
Introducing the AI for Healthcare Nanodegree Program!
Watch this video on YouTube
Udacity’s Nanodegree is a project-based online program designed for those who want to develop practical AI skills in a healthcare context. It is an advanced-level curriculum targeting data scientists and engineers (prerequisites include Python programming, basic machine learning, and statistics). The content is split into two main parts: applying AI to 2D medical imaging data (e.g. extracting and processing DICOM images, training convolutional neural networks on X-rays) and to 3D imaging data (like CT/MRI scans, volumetric analysis).
Throughout, students work on four real-world projects, such as building a pneumonia detection model from chest X-rays and writing an FDA approval plan, segmenting MRI images to assess Alzheimer’s progression, predicting patient outcomes for clinical trials, and integrating wearable sensor data for vital signs. The program is self-paced (most complete it in ~3-4 months) and offers mentorship, project reviews, and career services. Upon finishing, students earn a Nanodegree certificate.
Pros and Cons Hands-on coding with real data
Projects build strong AI portfolio
Self-paced with mentor support Requires ML and Python skills
No formal university credential
Subscription model can add up
Pricing
Subscription-based model (~$399 USD per month). Udacity recommends about 3 months to complete, so roughly $1,200 total, though learners who finish faster pay less. They often offer discounts or bundles (e.g. a 3-month package) and sometimes scholarship opportunities. All projects, mentor support, and career services are included in the cost.
Visit Nanodegree →
This University of Illinois Urbana-Champaign program is a short online certificate course (6 modules) aimed at healthcare professionals (physicians, nurses, PAs, etc.) who want a conceptual introduction to AI in medicine. It’s essentially a self-paced CME (Continuing Medical Education) course that can be completed in a few weeks (about 6–7 hours of content total), with up to 6 months of access allowed.
Through real-world medical case studies and examples, the course teaches how AI and machine learning models are used in clinical settings. It covers core concepts like how data-driven decisions are made, types of AI tools used in healthcare, and how to critically evaluate AI software for purchase or deployment.
The tone is non-technical and geared towards helping clinicians read AI literature confidently, understand AI outputs, and participate in implementing AI solutions in their practice. Notably, participants can earn continuing education credits.
Pros and Cons CME credits for clinicians
Great for AI beginners
Short and time-efficient format No programming or modeling work
Surface-level content only
Minimal peer or instructor interaction
Pricing
$750 USD flat fee. This includes 180 days of access to the online modules and the opportunity to earn the continuing education credits and certificate. Given the inclusion of CME credits, many clinicians find this a high-value, budget-friendly option to get started with AI in healthcare.
Visit UIUC Course →
Introducing the AI in Healthcare Program by Johns Hopkins University
Watch this video on YouTube
Johns Hopkins University offers this intensive 10-week online program designed to teach professionals how to leverage AI for improved healthcare outcomes. Delivered in partnership with industry (through the JHU Lifelong Learning platform), the course features a blend of live masterclasses by JHU faculty, mentor-led workshops, and self-paced modules.
The curriculum is broad and practically oriented: participants learn to rigorously evaluate AI models, design clinical AI trials, implement predictive analytics (including understanding how generative AI like large language models can support decision-making), and develop strategic action plans for integrating AI into healthcare organizations. Key topics include machine learning algorithms and performance metrics, ethical and regulatory considerations for AI (ensuring “responsible AI” use), healthcare data analytics (including graph/network analysis for population health), and leadership strategies to drive AI adoption at the enterprise level.
Students work on case studies and capstone exercises geared toward solving real healthcare challenges with AI. Upon completion, a Certificate of Completion from Johns Hopkins University is awarded, and graduates should be equipped to champion AI initiatives in clinical or administrative settings.
Pros and Cons Live instruction from JHU faculty
Focus on practical implementation
Covers genAI, ethics, leadership Premium pricing
Selective with fixed pacing
Broad but intense weekly content
Pricing
$2,990 USD for the full 10-week program. Includes live instruction, case studies, mentorship, and certificate.
Visit Johns Hopkins Course →
Choosing an AI in Healthcare Course
The intersection of AI and healthcare is brimming with opportunity – and these courses can help you seize it. Whether you’re a healthcare executive aiming to integrate AI solutions, a clinician seeking to understand AI-driven tools, or an engineer building the next medical breakthrough, there’s a course above tailored to your needs.
Investing in an AI in healthcare course can pay dividends: you’ll gain cutting-edge skills to improve patient outcomes, streamline operations, and drive innovation in your organization. Importantly, you’ll also join a growing community of professionals fluent in both healthcare and AI – a rare skill set in high demand (nearly 46% of clinicians report a shortage of AI talent in their organization (World Economic Forum). By upskilling now, you position yourself at the forefront of a revolution that is not only reshaping medicine but also saving lives. In short, if you want to be part of healthcare’s future, an AI in healthcare course is a wise prescription for success.
FAQs (AI in Healthcare Courses)
How can this Johns Hopkins AI healthcare course improve my clinical decision skills?
The course trains you to evaluate and apply AI tools that support clinical decisions—like risk prediction models, diagnostic algorithms, and decision support systems—so you can make faster, more accurate, and data-informed judgments at the point of care.
What ethical challenges will I learn to address in healthcare AI applications?
You'll dive into real-world issues like algorithmic bias, patient data privacy, model transparency, and compliance with HIPAA and FDA standards—preparing you to deploy AI responsibly and ethically in clinical environments.
How does these courses prepare me for implementing AI projects in real hospitals?
They cover the full implementation lifecycle—from identifying clinical pain points to selecting the right AI solutions, building cross-functional teams, navigating institutional approval, and managing change during deployment.
What practical case studies will help me apply AI to patient care and workflows?
You'll analyze case studies involving AI-driven triage systems, predictive readmission models, automation of routine tasks, and integration of AI into existing EHR platforms—giving you a clear view of AI’s operational impact.
Why is understanding machine learning algorithms crucial for my role in healthcare innovation?
A solid grasp of ML allows you to assess how algorithms work, validate performance metrics, detect bias, and ensure the models you adopt actually improve outcomes without compromising safety or equity.
| 2025-07-03T00:00:00 |
2025/07/03
|
https://www.unite.ai/best-ai-in-health-care-courses/
|
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AI in Healthcare: Career Scope in Australia | Monash Online
|
AI in healthcare: Career scope in Australia
|
https://online.monash.edu
|
[
"Rashid Elhouli"
] |
AI has the potential to transform how healthcare is delivered by providing advanced tools for diagnosis, treatment planning, drug discovery and patient care.
|
The development and adoption of AI in the healthcare industry is greater than ever before. According to a report by MarketsandMarkets, the global AI in the healthcare market is expected to reach $67.4 billion USD by 2027, growing at a compound annual growth rate of 46 per cent.
The impact of AI can also be seen in the healthcare job market. According to a report by the Australian Computer Society (ACS), ‘Australia is forecast to require an Artificial Intelligence (AI) specialist workforce of between 32,000 and 161,000 by 2030’.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://online.monash.edu/news/ai-in-healthcare/
|
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Qure.ai's AI solutions in the USA
|
AI assistance for Accelerated Healthcare
|
https://www.qure.ai
|
[] |
Qure.ai's AI solutions for ailments related to the musculoskeletal system, neurology, chest and heart help impact healthcare in the USA.
|
”Qure’s AI technology fits in all kinds of machines, new or old, making sure that we can use the old hardware and save our resources. By integrating Qure.ai with our existing X-ray systems, we can now screen patients promptly, obtain immediate results, and promptly refer clients for further investigation, ensuring timely management of TB cases.”
Llang Bridget M. Maama-Maime National TB Programme Manager at Ministry of Health, Lesotho
| 2022-12-01T00:00:00 |
https://www.qure.ai/
|
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|
Southwest General adopts AI to handle some healthcare tasks
|
Southwest General adopts AI to handle some healthcare tasks
|
https://www.cleveland.com
|
[
"Julie Washington",
"Jwashington Cleveland.Com"
] |
CLEVELAND, Ohio — At Southwest General Health Center in Middleburg Heights, artificial intelligence sends appointment reminders and does ...
|
Southwest General Health Center in Middleburg Heights is using Notable, an AI platform, to send appointment reminders and handle other tasks. John Benson/cleveland.com
CLEVELAND, Ohio — At Southwest General Health Center in Middleburg Heights, artificial intelligence sends appointment reminders and does other repetitive tasks so that healthcare workers can concentrate on patients, the health system said recently.
Southwest General is now using Notable, an AI platform that can fill out hospital admittance paperwork and make notes in electronic health records, the health system said.
The hospital system expects Notable to streamline operations, as well as improve staff efficiency and patient experience.
The platform is used at more than 12,000 healthcare facilities, according to Notable.
Southwest General joins other area hospitals that are using AI for administrative and surgical tasks.
The Cleveland Clinic in February began using AI technology to record conversations between caregivers and patients, and to generate written medical notes. The technology is used at some outpatient locations.
Also this year, University Hospitals began using a new tool, called Varian Ethos 2.0, to create precise, up-to-date images of the body, and accurately deliver radiation treatments.
At Southwest General, Notable AI will send reminders to confirm, cancel, or reschedule appointments and collect patient information needed for check-in. The platform also will contact patients, in their preferred language and communication method, to schedule appointments, the hospital said.
“At Southwest General, we’re committed to redefining the patient experience through innovation and compassion,” chief information officer Jae Zayed said in a statement.
“As a community-based hospital, our partnership with Notable allows us to make care more accessible and personalized—starting well before a patient steps through our doors,” Zayed said. “By streamlining appointment preparation and simplifying engagement, we’re excited to bring a more seamless, connected experience to the communities we serve.”
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.cleveland.com/medical/2025/07/southwest-general-adopts-ai-to-handle-some-healthcare-tasks.html
|
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Improving AI-Based Clinical Decision Support Systems and Their ...
|
Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders
|
https://medinform.jmir.org
|
[
"Godwin Denk Giebel",
"Pascal Raszke",
"Hartmuth Nowak",
"Lars Palmowski",
"Michael Adamzik",
"Philipp Heinz",
"Marianne Tokic",
"Nina Timmesfeld",
"Frank Martin Brunkhorst",
"Jürgen Wasem"
] |
Background: Artificial intelligence (AI)–based systems are receiving increasing attention in the health care sector. While the use of AI is ...
|
Conclusions: This study offers several possible strategies for improving AI-based CDSS and their integration into health care. These were found in the areas of “technology,” “data,” “users,” “studies,” “law,” and “general.” Systems, users, and the environment should be taken into account to ensure that the systems are used safely, effectively, and sustainably. Further studies should investigate both the effectiveness of strategies to improve AI-based CDSS and their integration into health care and the accuracy of their match to specific problems.
Results: In total, 13 individual and 2 double interviews were conducted with 17 experts. A total of 227 expert statements were included in the analysis. Suggestions were heterogeneous and concerned improvements: (1) in the systems themselves (eg, implementing comprehensive system training involving [future] users; using a comprehensive and high-quality database; considering usability, transparency, and customizability; preventing automation bias through control mechanisms or intelligent design; conducting studies to demonstrate the benefit of the system), (2) on the user side (eg, training [future] physicians could contribute to a more positive attitude and to greater awareness and questioning decision supports suggested by the system and ensuring that the use of the system does not lead to additional work), and (3) in the environment in which the systems are used (eg, increasing the digitalization of the health care system, especially in hospitals; providing transparent public communication about the benefits and risks of AI; providing research funding; clarifying open legal issues, eg, those related to liability; and standardizing and consolidating various approval processes).
Methods: Semistructured web-based expert interviews were conducted. Experts representing the perspectives of patients; physicians; caregivers; developers; health insurance representatives; researchers (especially in law and IT); and experts in regulation, market admission and quality management or assurance, and ethics were included. The conversations were recorded and transcribed. Subsequently, a qualitative content analysis was performed. The different approaches to improvement were categorized into groups (“technology,” “data,” “users,” “studies,” “law,” and “general”). These also served as deductive codes. Inductive codes were determined within an internal project workshop.
Background: Artificial intelligence (AI)–based systems are receiving increasing attention in the health care sector. While the use of AI is well advanced in some medical applications, such as image recognition, it is still in its infancy in others, such as clinical decision support systems (CDSS). Examples of AI-based CDSS can be found in the context of sepsis prediction or antibiotic prescription. Scientific literature indicates that such systems can support physicians in their daily work and lead to improved patient outcomes. Nevertheless, there are various problems and barriers in this context that should be considered.
Introduction
Background
The use of artificial intelligence (AI), especially machine learning, is becoming increasingly common in all areas of life. This is also evident in health care. In July 2024, the US Food and Drug Administration database included 882 AI- and machine learning–enabled devices from various medical fields such as radiology (n=671), cardiovascular medicine (n=90), neurology (n=32), hematology (n=17), gastroenterology and urology (n=13), anesthesiology (n=9), ophthalmology (n=9), clinical chemistry (n=8), general and plastic surgery (n=6), microbiology (n=6), pathology (n=6), or orthopedics (n=5) [ ]. AI is believed to have tremendous potential to revolutionize patient care and outcomes [ , ]. This is especially because of its ability to support diagnosing, individualization of treatment plans, and clinical decision-making [ , ]. AI-based clinical decision support systems (CDSS) offer the potential to support physicians in their work and to optimize patient outcomes. For example, this is evident in the context of sepsis. In this context, such systems can support prediction, diagnosis, subphenotyping, prognosis, and clinical management [ ]. This can lead to earlier treatment [ ] and ultimately to shorter hospital stays [ , ] as well as reduced mortality [ - ].
Given these potential positive effects, it is questionable why AI-based CDSS have only been used in isolated cases in real care. One possible reason could be the barriers to establishing such systems: regulators must approve them. The systems must be integrated into electronic health record systems, and they must be standardized to the extent that similar products operate in a similar way. Clinicians must be trained to use them, they must be updated over time, and payment must be organized [ ]. Furthermore, user perception is a fundamental criterion that decides acceptance [ ].
The literature describes a further wide range of problems and barriers in the context of AI-based CDSS [ - ]. These relate to AI or CDSS and a combination of both, AI-based CDSS. While some of the problems relate to technical integration and operational use [ , ], others relate to the legal and ethical framework [ ]. Regarding strategies for implementing AI-based CDSS in clinical practice, a study conducted by Peek et al [ ] analyzed the literature and concluded that perspectives from groups other than health care professionals (eg, patients, carers, AI developers, health care managers, and leaders, and especially regulators and policy makers) should be investigated.
Objective
To investigate the perceived problems and possible improvements involved in integrating AI-based CDSS to health care and to develop a user-oriented requirements profile for the systems, we established the research project “KI@work” [ ]. It was founded by the German Federal Joint Committee (01VSF22050).
As part of the project, a scoping review (Raszke P, unpublished data, April 2025) was conducted to collect fundamental evidence. On the basis of the results, we conducted interviews with experts to further investigate the following questions: (1) What problems and barriers hinder the integration of AI-based CDSS into health care? (2) What approaches might be used to enhance the quality of AI-based CDSS and their integration into care? While the first question was covered in a previous article [ ], this paper especially focuses on the second. Thus, this study aims to gather expert opinions on how AI-based CDSS and their integration into care can be optimized.
Methods
Overview
Expert interviews were conducted to identify approaches to solve perceived problems in the context of AI-based CDSS. We followed the standards of O’Brien et al [ ] and the 32-item COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist ( ) [ ] in preparing this manuscript. Therefore, transparency is guaranteed in all aspects of our study. The underlying interviews and corresponding transcripts, have already been examined for existing problems and barriers in the context of AI-based CDSS from the perspective of experts.
Theoretical Framework
There is some evidence of problems and barriers, but as the field of AI-based CDSS is relatively new, little is known about how to optimize its use. Therefore, qualitative research in the form of expert interviews was conducted to approach the topic in an explorative way. The interviews were conducted with experts in the context of AI-based CDSS. The interviewees represented a wide range of different areas and professions. The interviews had 2 objectives, and the proceeding remained consistent: they started with open questions and topics and then addressed more specific questions. Within each topic, we first asked about different problems and barriers in the context of AI-based CDSS. Then, we asked about ways to improve care with AI-based CDSS.
Ethical Considerations
Because patient views were collected through representatives and not from patients themselves, and no personal data were collected, the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen confirmed that no application for ethics approval was required. Before the conversations, experts were informed via email about the topic and the proceeding. The email also included information about privacy and that the conversations would be recorded and transcribed for data analysis. Furthermore, the interviewees were informed that they could end the conversations on their part at any time without giving a reason and without experiencing any disadvantages. All experts provided consent to participate. The interviews were recorded and either transcribed automatically in Microsoft Teams (Microsoft) or manually for Zoom (Zoom Video Communications) interviews. Revision and pseudonymization were subsequently performed by research assistants and quality-checked, each by one of the moderators. After quality checking, the transcripts were sent to the respective experts for review to ensure the accuracy of the content. Three experts made use of this option, and their feedback was incorporated into the transcripts. The video recordings were then deleted. Therefore, it is not possible to assign individual statements to individual experts unless recalled by participants or moderators.
Participant Selection and Preliminary Information
The question “Who are the relevant stakeholders in the context of AI-based CDSS?” was discussed during a project meeting to create a list of experts to contact. The list included representatives of patients; physicians; caregivers; developers; statutory health insurance funds; researchers (especially in law and informatics); and experts in regulation, market admission, quality management or assurance, and ethical issues. On the basis of this list, appropriate institutions were sought and contacted to find eligible interview partners. If a consortium member had a connection to one of the identified institutions, they helped establish contact with a potential interview partner (n=7). Five (71%) of the 7 people contacted participated in an interview. In 2 (29%) cases, the person originally contacted arranged an interview partner within their institution. The remaining 8 interviewees were recruited independently of personal networks. In all cases except one, it was ensured that the interviewers had no prior contact with any of the interviewees.
Possible participants were contacted directly via email by the consortium. After contacting the experts, none of them declined to take part in the study. Four experts did not respond to the request. Ultimately, all target groups were represented in the interviews.
Setting and Conducting the Interviews
All interviews were conducted on the web via the conferencing platforms Microsoft Teams (Microsoft) or Zoom (Zoom Video Communications). Interviews included 1 or a maximum of 2 experts at a time. Circumstances or events which had an unintended influence on the conversations are not known. The interviews were moderated by 1 person and attended by at least 1 other person from a pool of 3 possible moderators (GDG, PR, and NB). The characteristics of the moderators are listed in . Notable relationships did not exist between the researchers and the interviewees. There was no clear position for or against the use of AI-based CDSS in health care.
An interview guideline was developed (based on the preliminary results of a scoping review conducted before the interviews) by the team of the University of Duisburg-Essen and quality was assured within the consortium. The guideline included questions about both problems and barriers in the context of AI-based CDSS, as well as questions about possible improvements. It was slightly adapted with stakeholder-specific questions. The core version can be found in . The interviews took place between June and August 2023.
Data Analysis
A content-structuring qualitative analysis following the study by Kuckartz [ ] was conducted using MAXQDA (VERBI Software GmbH) based on the final transcripts. The analysis aimed to conduct a content- and topic-oriented evaluation, as well as the development of main topics and subtopics. The analysis was based on deductive codes to create an overarching systematization. These included six categories in which approaches for optimization were suggested: (1) “technology,” (2) “data,” (3) “user,” (4) “studies,” (5) “law,” and (6) “general.” After deductive coding, relevant expert statements were concisely summarized (GDG) and subsequently discussed in a workshop (GDG, NB, and PR). The workshop aimed to develop finer-grained groups for a more differentiated perspective. Similar statements within the 6 overarching categories were grouped into subcategories (inductive codes), which were then used for further systematization. Finally, the expert statements were coded according to the inductive codes and systematized within a matrix using Excel (Microsoft Corporation; ). Data analysis was finished in October 2023.
Results
Overview
In total, 17 experts were interviewed in 13 individual and 2 double interviews. The professional backgrounds of the experts are provided in . The intrinsic motivation and participation of the experts were high.
Table 1. Conducted interviews. Number Stakeholder Setting Date 1 Caregiver representative Individual interview June 5, 2023 2 Representative of quality management Individual interview June 5, 2023 3 Researcher—AIa in health care Individual interview June 6, 2023 4 Medical product consultant Individual interview June 16, 2023 5 Caregiver representative Individual interview June 27, 2023 6 Caregiver representative Double interview June 27, 2023 7 Representative of quality assurance Double interview July 4, 2023 8 Representative of an ethics committee Individual interview July 17, 2023 9 Researcher—Social and health law Individual interview July 19, 2023 10 Medical product consultant Individual interview July 27, 2023 11 Developer of AI-based CDSSb Individual interview July 27, 2023 12 Patient representative Individual interview July 31, 2023 13 Physician representative (Intensive care) Individual interview August 8, 2023 14 Developer of AI-based CDSS Individual interview August 11, 2023 15 Representative of health insurance fund Individual interview August 17, 2023 aAI: artificial intelligence. bCDSS: clinical decision support systems.
A total of 227 expert statements included suggestions for improvement of AI-based CDSS and their integration into care. As described in the Methods section, these were categorized into 6 categories: “technology,” “data,” “user,” “studies,” “law,” and “general.” The systematization is presented in . The complete and systematized list of summarized expert statements is presented in .
Figure 1. Identified improvement approaches matched with problem categories. AI: artificial intelligence; CDSS: clinical decision support systems.
Technology
Optimization potential in the category of technology included “quality,” “transparency,” “updates,” “individualization,” “usability,” “development,” and “control of users.” Experts explained that the “quality” of AI-based CDSS is a fundamental factor affecting trust and acceptance over time. Therefore, requirements for high-quality systems should be considered. According to the experts, such requirements include that the systems be evidence-based and reliable and meet at least the current gold standard for value. Quality and validity assessments should be conducted regularly to maintain good quality over time. This should include checking the system against a validation dataset, sampling procedures, or test loops. The quality of systems could also reduce the burden of false alerts. Therefore, on the one hand, the systems should be able to identify false-positive and false-negative results. On the other hand, an appropriate balance between sensitivity and specificity might avoid unnecessary alerts as well as associated alert fatigue. Finally, incorrect recommendations can be avoided if systems clearly stick to evidence-based or, at least, consensus-based guidelines:
That means, the only reasonable method to increase acceptance, is to increase the quality of products and to gain trust in this way. That we currently have very low acceptance in some areas, maybe that’s a good thing and that it must increase over the next years is due to the innovative technology. A normal process.
[Medical product consultant]
“Transparency” can be guaranteed by avoiding black-box characters (incomprehensible AI). According to experts, AI-suggested decisions should be transparent, and systems should provide explanations. This could be achieved by identifying decisive parameters or using so-called heat maps to visualize data and give at least clues as to where the proposed decision stems from. Finally, it was mentioned that self-learning systems should stop evolving when they are no longer comprehensible:
So, the idea is actually that the decision that the AI gives me is justified by the AI in such way that I can, in a way that is so clear and comprehensible that I can perhaps also understand mistakes in the decision-making chain.
[Caregiver representative]
Experts stated that “updates” must be conducted to include new data and guidelines as well as to adapt the algorithm accordingly. Sensible implementation of user feedback should be considered during updates as well:
So, I demand diligence from the AI, but I also need the teacher somewhere, who must have a learning concept, the algorithm must always be continuously checked and adapted.
[Representative of quality assurance]
Experts with a more technical background saw “individualization” as a way of enhancing the quality of the systems. They explained that systems must be adapted to individual hospitals and that models from the United States should be optimized for local use in Germany. A physician representative, by contrast, stated that system standardization would be necessary to enable system use across locations:
I believe that you can’t do it preclinically. I have to talk to the hospitals, I have to set up the project in the hospital, in the care setting. Because [...] we have 1,200 ecosystems, if I’m only talking about acute hospitals now [...]. I have to involve the doctors in each hospital individually. I have to involve IT in every hospital.
[Developer of AI-based CDSS]
A further suggested approach lies in “usability.” User-friendliness should be considered to facilitate use and point users directly to relevant points. This includes the convenient use of AI-based CDSS for bedside conversations. User skills, as well as knowledge gaps, should be considered within system design. According to experts, a thorough “development phase” with a clearly determined question or target problem could contribute to the prevention of problems in the context of AI-based CDSS. The systems should be extensively and sufficiently developed and trained. False-positive and false-negative results must be checked during the training phase. The causes of identified errors must be eliminated. A balance between sensitivity, specificity, and overfitting (ie, training too specifically based on a fixed dataset) should be maintained. An interdisciplinary development, including experts and stakeholders from various fields (eg, future users, physicians, IT managers, and patient advocates) was desired. Involvement could include statements, tests of prototypes, participatory development, conceptual design, and open discourse. The development process should always be controlled by an independent process attendant:
I believe that it is crucial that, when you develop something AI-based like this, the people who will have to work with it later, who will have to use it, are involved as much as possible, right from the development phase.
[Caregiver representative]
Finally, “control of users” could improve the quality of AI-based CDSS and their integration into care. The intelligent design, implementation, and testing of risk control measures and their corresponding implementation in AI-based CDSS could prevent blind trust and automation bias. Furthermore, deliberately incorrectly proposed decisions by AI might be used to check the awareness of users:
Problems like this [such as automation bias], can be countered by designing and implementing intelligently enough, I would say.
[Caregiver representative]
Data
A further starting point for improvement is the area of “data.” Approaches in this context concern the “quality,” “quantity,” “standardization,” and “availability” of data.
First, according to the experts, high data “quality” must be guaranteed. Therefore, the quality of the training data should be improved. This can be done through data management, reflective data synthesis, or reflective filling of incomplete datasets. The validity of the primary dataset should be made transparent:
So that would be a very important point for me, especially at the beginning of the study protocols, from our experience, also in terms of quality assurance, to be able to make a statement about the validity of the primary data pools.
[Representative of quality assurance]
“Quantity” included the enlargement of the database as well as its completeness. According to experts, both would contribute to better results. Structured and unstructured data should be included, and longitudinal data should be used in the analysis. No data should be excluded because of the age, origin, or sex of patients. Databases should be equally weighted, sufficiently represent women as well as minority individuals, and consider age and diseases:
And of course it is very important that we have equally weighted data sets, that we represent minorities and so on. But that’s a craft, it can be done. It has to happen, yes, but it’s not inherent in the technology, it’s simply a manual part of using the technology.
[Researcher—AI in health care]
A further approach was seen in “standardization.” Experts asked for national-level data standardization and integration. Therefore, data could be merged and made usable. Generic data patterns should be predefined:
[...] yes, I like, this generic character of data sets. This is, I rather think, the field, you have to create generic data patterns that hospitals have to adhere to in order to achieve accuracy.
[Developer of AI-based CDSS]
Finally, the “availability” of data could contribute to improving the systems and their integration into care. Experts suggested the creation of a national or European data pool, as well as networks, to obtain sufficiently large datasets. Data should also be accessible for the private sector to develop AI-based CDSS. Analogous data from hospitals should be made available using machine reading, such as optical character recognition, of PDF documents such as scans or photos. During the digitization process, experts should be involved in reading out subject-specific documents:
And that means we should take the EU Commission’s proposal with the draft regulation on the European Health Data Space seriously so that private sector companies that develop AI-based CDSS can also access such data. Because every year we have 550 million outpatient data records and 15 million inpatient data records that are not accessible for this development or not easily accessible.
[Medical product consultant]
User
Many of the identified improvements are user-oriented. These include “training/education,” “time relief,” “critical attitude,” “communication,” and “competence.”
First, “training/education” might lead to more openness to AI-based CDSS. Therefore, information on AI should be a part of both medical training and further staff training. Staff training should include educational courses or workshops on the general topic of AI, its threats (eg, errors in its use, especially automation bias), its limitations, and information on its impact on existing care pathways. Courses should provide the opportunity to try out AI-based CDSS. Their application under stress should be exercised. In contrast to most of the other respondents, a developer stated that only those who modify the systems needed training but not those who just use the systems. A physician representative stated that trainings and courses should be nonmandatory. Experts pronounced that, within training, it should be clarified that AI will not replace humans:
And I think what would help to increase that acceptance would be just regular training. If you could also show examples of what a specific case might look like.
[Caregiver representative]
And I believe, if there are no training courses or no sensitization, then there is a risk that such recommendations for action will simply be adopted and that would be disastrous.
[Developer of AI-based CDSS]
Experts saw “time relief” of physicians and caregiving professionals as a solution for missing acceptance. They stated that the temporal valences of users should be considered and that AI should not lead to major additional work. Rather, it should make day-to-day work easier, for example, by supporting documentation. Furthermore, it was explained that clinicians should be relieved for scientific tasks in the context of AI and use their competence to assess complex cases:
[...] that you have a mixture of easier and more difficult cases, and AI will tend to mean that the easy cases can be checked off very quickly by the AI or with the help of the AI, and that only the very difficult cases will actually end up with the doctors. And maybe there needs to be some relief here. You have to say that somehow you have to take more breaks [...] because looking at the screen all day and then only having to differentiate between the worst cases is not easy.
[Representative of an ethics committee]
A further improvement approach was seen in the “critical attitude” of users. It was stated that decisions suggested by AI must always be scrutinized, validated, and checked for plausibility. There should be no blind reliance on AI. One expert explained that regardless of whether recommendations are made by AI or humans, they should always be questioned. A critical attitude was described as relevant to both positive and negative findings. Thus, even supposed false alarms should be taken seriously. One stakeholder wished for mutual skepticism between AI and users:
This means that the suggestions or recommendations for action of an AI-based CDSS must be evaluated by the doctor in the same way as a laboratory value or another parameter or a pathological ECG. It must be critically scrutinized; it must be validated [...].
[Medical product consultant]
In addition, an opposing view was taken in the context of “critical attitude”: users should gain greater trust in AI and, in some future cases, might also implement recommendations for action without checking them:
And at some point, we will receive recommendations for action that we can rely on 100% and then we can implement them without checking them. So, the answer to your question is: we encounter the problem by only implementing recommendations for action unchecked where we have a sufficiently high level of certainty that it will work due to the validation.
[Medical product consultant]
A last improvement approach concerning the user was seen in “competence.” According to experts, users need personal and professional competence to use and deal with decisions suggested by the AI, as well as to maintain decision-making authority and avoid dependence on AI. In particular, the implicit knowledge from experience should be maintained. Finally, interviewees also explained that trust increases with competence:
So, who decides and who does not, I think it has something to do with education, it has something to do with personal competence and of course it also has a lot to do with professional competence.
[Caregiver representative]
Studies
Regarding “studies,” improvement approaches were named within 3 areas. These were “study conduction,” “external audit,” and “financing.”
“Study conduction” was seen as essential to prove evidence, to assess benefits, and to track side effects. Demonstrating success in studies would lead to increased public acceptance and trust. A general need for more valid (comparative) and prospective studies was pronounced. A “clean, best possible study design depending on the disease” [Representative of health insurance fund] was demanded. Orientation of studies on standards of scientific institutes and the involvement of experts was described as necessary:
We now need prospective randomized studies, one part gets standard care, one part gets AI and then we have to look and that has to be done on a relatively large number of patients across several sites so that we can see whether it is possible to detect organ dysfunction and sepsis in advance.
[Physician representative—intensive care]
In general, of course, the higher the quality of the study design - and a randomized controlled trial is of course the gold standard - the more reliable the results are.
[Representative of health insurance fund]
Experts stated that studies should also include an “external audit” to get a neutral view of the situation. Furthermore, involving patient organizations in studies can help increase acceptance among patients. Finally, in this context, an improvement approach was seen in the “financing”—specifically, the funding of studies. The federal or state governments or the European Union (EU) were seen as responsible for study funding:
We must now have this willingness to carry out these studies, [...] I mean we’re talking about a few million euros, [...]. That is now indicated, that is the next logical step and if we were to do that, we would be very far ahead, not just in Europe, but globally.
[Physician representative—intensive care]
Law
“Approval/certification,” “data protection,” and “liability” were seen as possible improvement approaches within the category of “law.” Further suggestions within this category were aggregated under “others.”
“Approval/certification” and review of AI-based CDSS should be conducted regularly by independent bodies to increase acceptance. It was expressed that certifying bodies would need more staff with a background in science, technology, engineering, and mathematics. Approval of AI should not lead to major additional costs compared with conventional medical devices. Another approach expressed was a certification that authorizes humans to use AI:
I think independence is important. An established, independent institute and regular repetition, [...] and AI systems are also constantly changing, because they are - AI means a self-learning system, which means that you have to check them every now and then. So regularly, independently and in an understandable way.
[Patient representative]
Further opportunities were seen in the regulation of “data protection.” Data protection standards should always be appropriate, and data integration should be standardized nationwide. Users could benefit from already certified plug and play hardware solutions to avoid data protection issues. Whether researchers are allowed to use the collected data should be decided by the patients. Furthermore, experts stated that problems in the context of data protection are often caused by data protectors and not by data protection requirements and are thus avoidable:
Every data protection officer has a problem with personal data that goes off campus, and I absolutely understand that. But what I always like to ask is, has anyone ever asked the patients whether they could possibly care less? [...] There are clinics that do this in Germany, they use all their patients’ data for research purposes. It’s not forbidden, I’m just saying opt-in, opt-out.
[Developer of AI-based CDSS]
Regarding “liability,” experts have suggested that modes of action and responsibilities should be clarified. In this context, it was proposed that it should be legally anchored that diagnostic and therapeutic responsibility remains with the user, specifically the physician. Users should be informed about the legal framework. Otherwise, one expert explained that physicians should not be made liable for AI-proposed decisions that cannot be verified because of a black-box character of the systems:
And liability risks are also a major issue, and doctors naturally don’t want to be held liable for anything, for possible treatment errors or that they have overlooked something if they can’t actually assess the quality of this AI. And that’s why I would say that increasing acceptance is also possible through regulation.
[Representative of an ethics committee]
A further two improvement approaches were described. These were the obligations to report and publish errors that occur in the context of the systems in general and to report and track, especially, the causes of incorrect decision recommendations.
General
Under the category “general,” various suggestions for improvement were summarized that did not match one of the other categories. These include “level of digitalization,” “financing,” “communication between stakeholders,” “information about AI,” “new professions,” “pretest,” “user definition,” “no staff replacement,” and “others.”
The “level of digitalization” should be expanded for IT infrastructure. There is a demand for high-performance computing to ensure fast, reliable, and failure-free operation of the systems. Digitization should be standardized to close gaps in the documentation:
And I remember that from before, when we had the paper curve, we really had a lot of gaps in the documentation as far as vital signs were concerned. So that’s why we should have uniform digitization, which would of course be the optimum.
[Caregiver representative]
Improvement in “financing” concerned 3 aspects. First, service providers should be motivated with incentives to use AI-based CDSS. Second, incentivization could lead service providers to collect data more conscientiously. Third, experts saw the federal states and the German social policy as responsible for participation in hospital financing and the financing of digitalization infrastructure of the health care system:
But on the whole, people act as if this is purely a private matter. [...] My personal opinion would be that German social policy should contribute to the financing of infrastructure within the digitalization of the healthcare system in particular [...].
[Caregiver representative]
“Communication between stakeholders” could be improved by using a common language between the professions. Experts suggested appointing a chief medical IT officer to build bridges between stakeholders and drive the integration of AI:
It’s not down to our doctors’ willingness to innovate, on the contrary, it’s a lot down to IT and a lot down to the data protection officer and a lack of communication within the hospital. My hospital is divided into two departments. On the one hand we have white, everything that is medicine and on the other hand it is a normal company. That’s why I think one of the most important positions in a company is the CMIO, the Chief Medical IT Officer, who has a view from both sides. And he can also build a very big bridge.
[Developer of AI-based CDSS]
Another much-discussed improvement category is “information about AI.” In this context, communication should include both the benefits and the limits of AI. According to experts, education and transparent information would create acceptance and trust and would avoid exaggerated expectations. Communication should be tailored to the target group. It should convey skepticism toward technology to younger people and introduce older people to the technology. Stakeholders wished for clarification on the fact that the technology can save time and does not take away expertise. Therefore, one should communicate that the outputs are basically suggestions, and the decision remains with the physician. Showrooms and practical examples should be used to convince people of the benefits and to create acceptance:
[...] I believe that it is very important for acceptance that you manage to make it clear that it is a system that can ultimately save time, which is then regained by the doctor for the patient.
[Representative of quality assurance]
We should certainly try to educate users and we do this by holding seminars, for example, where we remove the magic from AI and explain that it is machine learning. We also explain how it works and where the limits are. Because, as I always like to say, “there is no free lunch.
[Researcher—AI in health care]
“New professions” could also contribute to improvement. One expert explained the need for new professions to guarantee high data quality:
And I believe that if we don’t have new professional fields that can actually be called data secretaries or whatever, i.e. all those who actually ensure that the quality of the data is excellent, we will just have to make do with bad data and then also with bad results.
[Representative of an ethics committee]
Before the system becomes distributed, a “pretest” should be done in predefined settings. A system running in the background should serve to identify and check discrepancies in decisions between humans and the systems. Dissemination should begin in expert institutions to achieve a certain standard of quality and should always proceed from the most to the least competent users. During this process, systems should learn from mistakes:
That’s why there’s a phase of around six months where the system runs in parallel, in the background, where a comparison is made between what the machine would have decided and what the doctor would have done. A machine also learns from this so that it can then say when the system is introduced - I would never do an untested implementation.
[Developer of AI-based CDSS]
[...] at some point a state will be reached, a kind of steady state, in which it is very mature, [...] and then you can go further, but I think it is very important in the first few years to work together with the competent people and not to present it as a help that those who have resource problems or qualification problems can now do things with it that they would otherwise not be able to do.
[Representative of quality assurance]
In the context of AI-based CDSS, it should always be clear who the target users are. Therefore, the developer should clearly offer a “user definition.” Experts explained that systems should neither be used to “replace staff” nor to make up for a lack of specialist staff. Ideally, there should be teamwork or cooperation between the AI and users. AI-based CDSS should not undermine the expertise of physicians. While the systems should aim to support users, they should not overtake their task of decision-making:
Furthermore, I think it is important that we do not immediately say, yes, there is great dynamism, we can replace the missing specialist staff in a few years or save on staff now and eliminate an economic deficit.
[Representative of quality assurance]
Finally, two other possible improvements were seen in this category. First, software consulting firms should always be available as contact partners. Second, a self-fulfilling solution was seen over time. This was seen because, on the one hand, the use of good systems will automatically lead to acceptance and, on the other hand, because future generations are more open to AI.
Discussion
Principal Findings
The results of this study show that there is already a wide range of suggested improvements in the context of AI-based CDSS. These exist in 6 areas: “technology,” “data,” “user,” “studies,” “law,” and “general.” A structured overview of all identified solution strategies can be found in . As in other studies on AI-based CDSS, improvements were found that address the technology itself, the users, and the context in which the systems are used [ - ]. Among others, the recommendations concerning the systems encompass the following aspects: a comprehensive developmental and evaluative process with the active engagement of users before the implementation; systematic training on an extensive, high-quality database; rigorous studies proving the system benefits; optimized ease of use and accessibility; customized systems tailored to specific settings, hospitals, and patient populations; and the incorporation of control mechanisms or the use of intelligent design and smart implementation to mitigate automation bias. On the user side, enhancements were suggested to the training program for (future) users. Training should include communication about the advantages as well as sensitization for critical thinking. Temporal valences of the users should be considered; systems should alleviate the burden on staff rather than augment their workload. Finally, regarding the context, experts suggested strategies such as increasing the level of digitalization of the health care system, especially hospitals; transparently communicating in public about the advantages and risks of AI; providing funding for research; clarifying open legal questions, for example, with regard to liability; and standardizing and consolidating various approval processes.
There are already some studies that investigate factors enabling or facilitating the use of AI-based CDSS. These stem from various fields such as antibiotic prescription in hospitals [ , , ], rhythm management of atrial fibrillation [ ], red blood cell transfusion [ ], and aortic dissection [ ]. For example, they focus on health professionals [ , - ] or hospital managers [ ]. Our study was not aimed at a special medical field or a single group of experts. Therefore, we were able to investigate indication-independent improvements from a broad range of different experts. This is especially important because several complex problems need interdisciplinary cooperation to be solved.
According to the experts interviewed in our study, a high-quality and user-friendly design of AI-based CDSS should be guaranteed. In this way, problems occurring during use could be prevented. One such problem is automation bias. To prevent this issue, systems should be designed with a well-thought-out design. This includes system outputs with fewer on-screen details, dynamic updates of the confidence levels of recommendations, and the provision of supportive information rather than orders [ ]. Another example mentioned by experts and confirmed by the literature was transparency. Trust from physicians can be gained by providing as much transparency as possible [ ]. An example not restricted to AI-based CDSS but relevant to digital solutions, in general, is the topic of usability. By integrating future users at every stage during the design process, ease of use can be enabled [ ].
Both manufacturers and policy makers are in charge of guaranteeing a high quality of AI-based CDSS. While manufacturers should have an intrinsic motivation to develop high-quality systems, policy makers should set the framework conditions.
The EU first took the step of introducing the AI Act [ ]. A prerequisite for the development of reliable AI-based CDSS is the availability of sufficient quality data. Therefore, experts stated that data should be collected in a standardized format and made available to researchers as well as private companies. One example of a large collection of physiological and clinical data is the PhysioNet platform, managed by members of the MIT Laboratory for Computational Physiology [ ]. A German project funded by the EU is the SepsisDataNet.NRW [ ]. One of the project’s goals is to create a sepsis-related biomarker database. From a legislative perspective, the German Bundestag adopted the Act on the Improved Use of Health Data (Health Data Use Act [Gesundheitsdatennutzungsgesetz]) [ ]. At the European level, it is planned to create the “European Health Data Space,” an initiative to enable patients to easily control their electronic health data and to make this data available for research, innovation, and political decision-making [ ].
Experts explained that the perception of users should be positively influenced. There is little experience with the new technology. In a survey conducted by Chen et al [ ], only 27% of the physicians and medical students surveyed worldwide had used clinical AI. A total of 13% reported having good knowledge of AI. Furthermore, 77% expressed a high willingness to learn about the topic, and 78% agreed that training should be provided by hospitals or schools. Only 8% feared that physicians would be replaced by AI [ ]. The low level of knowledge, together with a great openness [ , ] to the topic, provides an ideal basis for education. In this context, a special focus should lie on scrutinizing proposed decisions to prevent blind trust, automation bias, and alert fatigue.
Regarding studies, interviewees demanded more prospective randomized studies. This aligns with the existing literature. In 2022, a systematic review of randomized controlled trials (RCTs) of AI in clinical practice identified only 39 eligible studies and concluded with the need for more RCTs of AI-assisted tools integrated into clinical practice [ ]. To enable such studies, national or international bodies should provide funding.
According to experts, regulation in the context of AI-based CDSS has not yet been fully elaborated. Questions regarding the trade-off between data protection and patient benefit, regarding the liability and the certification of AI-based CDSS, should be the subject of further discussion and research. For example, there is no uniform opinion between physicians and patients regarding liability for AI in health care [ ].
The spectrum of general improvements proposed by experts was broad. Two topics should be particularly underlined. First, in Germany, where the interviews took part, many hospitals are still not sufficiently digitally equipped to enable the use of AI-based CDSS. Given the combination of the high costs of digitalization and the financially strained situation of most hospitals in Germany, the burden cannot be borne by the hospitals alone. Therefore, more funded initiatives, such as the SmartHospital.NRW initiative are required. The aims of this initiative are, on the one hand, to develop a process model that can be transferred to hospitals with different levels of digitalization and, on the other hand, to develop and test innovative, AI-based applications for real-world use-case scenarios [ ]. Second, interviewees expressed a high need for education, not only for medical professionals but also for the public. Even if the attitude toward AI is generally positive, there are also some concerns or perceptions. For example, consumers fear the dehumanization of the clinician-patient relationship, see a threat to shared decision-making involving patients, assume low trustworthiness of AI advice, and express uncertainty around fairness and equity in treatment allocation [ ]. These concerns should be taken seriously and addressed through open and transparent communication about the opportunities and limitations of AI.
Implications
The suggested improvements should not be used without reflection because there is a risk that improvements will result in further problems. For example, experts suggested that information about the benefits of AI should be given. Thereby, on the one hand, acceptance could be improved, but on the other hand, there is a potential risk that users have too much confidence in the systems and are prone to blind trust, respectively automation bias. Another example is the critical attitude of users. Some experts stated that every decision proposed by the systems should be scrutinized. While, in general, this sounds comprehensible, it bears the risk that the systems might not lead to a significant reduction in workload and thus might become rejected by physicians.
Even if our research provides a variety of different improvement approaches, we did not search for an ideal match between existing problems and improvements. Even if, in some cases, the matching is obvious (eg, a lack of transparency could be reduced by providing decisive parameters), there are more complex interrelationships between some problems and solutions. For example, there is no direct solution for discrimination. Rather, various improvements should be considered here. An optimization of the system itself, the underlying database, or raising user awareness of the problem could be possible approaches. Therefore, further studies should be conducted to investigate such relationships.
The hierarchical framework from the study conducted by Kaiser et al [ ] might serve as a starting point to identify the key implementation barriers and improvements that must be considered when introducing AI-based CDSS. According to this, the top level requires an effective implementation strategy (strategy level), the middle level should consider the capabilities and resources needed to successfully integrate an innovation, and finally the bottom level focuses on the implementation and change of processes and routines that are needed to accommodate the innovation.
Recommendations
On the basis of the results of this study, some actionable, solution-oriented recommendations can be proposed that could be practically implemented by stakeholders ( ). These recommendations are not specific to the German health care system, as they address cross-cutting challenges, such as data quality, user behavior, regulatory complexity, and clinical validation—issues that arise independently of national health care structures. Their relevance extends to many countries facing similar demands in safely and effectively integrating AI-based CDSS into routine care.
Table 2. Solution-oriented recommendations based on this study. Number Recommendation Problem Solution 1 Postmarket monitoring AIa-based CDSSb should rely on the current state of medical knowledge. However, medical knowledge is subject to constant change, leading to concept and data drifts. Consequently, the original assumptions on which systems are based are no longer up to date, potentially reducing their accuracy or even their validity. Regularly investigating the quality and reliability of AI-based systems might uncover drifts at an early stage. These verifications should not only be of a purely technical nature but also include actual use in care.c 2 Control mechanisms A severe threat in using AI-based CDSS is automation bias. Control mechanisms within the systems could be used to encourage users to think and avoid blindly implementing recommendations. One possible approach might be to provide system-based recommendations only after the assessment of the user.d 3 Comprehensive data pools The quality and quantity of data are considered major problems. This may be particularly true for rare diseases. Building supranational data pools can provide more data to train reliable AI-based CDSS. Common data standards, such as FHIRe, can support standardization. However, the special characteristics of health care systems and patients, as well as other potential factors arguing against generalization, should also be considered.f 4 User training The perception of AI differs greatly. The spectrum ranges from exaggerated fear to unrealistic expectations. There are problems in both directions: either the systems are rejected, or users trust them blindly. By integrating AI into the curricula of medical faculties, future users can engage with the technology in a more informed way. This could provide the skills to critically engage with AI-based CDSS. Thus, both automation bias and irrational fear can be met at an early stage. 5 Implementation of studies User acceptance presents a possible hurdle for the introduction of AI-based CDSS in health care. While studies could be convincing because of the proof of benefit, the implementation of studies is associated with high costs. Studies are essential to establish the evidence of AI-based CDSS. While current studies mostly focus on the accuracy of the algorithms (eg, recall and precision), future studies should also examine the actual benefits of their use in a clinical setting. Preferably, this should be done with RCTg studies. However, as such studies are costly, they should be financed by national or supranational funds. Such an approach would enable universities and smaller independent institutes, in particular, to conduct research in this field. 6 Harmonization of approval procedures The approval of medical systems is already associated with high effort, including outside Europe. This affects not only developers but also certifying bodies. Future legislation (eg, the AI Act in Europe) should consider and harmonize existing regulations. This could significantly reduce workload and documentation burden. Possible areas include postmarket surveillance, risk assessment, safety and performance requirements, and technical documentation. aAI: artificial intelligence. bCDSS: clinical decision support systems. cThe European Union is introducing such postmarketing monitoring for high-risk products as part of the AI Act. The actual implementation is planned for February 2026. dThe actual design requires further research to prevent users from using “dummy inputs” to get recommendations. A cooperative process between the user and AI might be a promising approach. eFHIR: Fast Healthcare Interoperability Resources. fThe European Union recently decided that such a data space (“the European Health Data Space”) will be created for the member states. gRCT: randomized controlled trial.
While these recommendations appear well-founded and may offer valuable insights for optimizing AI-based CDSS and their integration into health care, it is important to emphasize that they are derived from a qualitative, exploratory approach. Therefore, they should be considered preliminary and require further investigation and validation. This will be addressed in the context of our overarching research project “KI@work” [ ]. On the basis of the results of these expert interviews, supplemented by a literature review, focus groups with physicians and nursing staff, a quantitative questionnaire survey and workshops with experts, and well-founded recommendations for action are to be developed that are more binding in nature.
Limitations
Some limitations of our research should be mentioned. First, we conducted a qualitative interview study. Therefore, the results are more exploratory than deterministic. In addition, the attitudes, values, and experiences of the interviewers may have impacted the final results. To minimize this risk, only a structuring and no further interpretation of the results was carried out. In addition, we have presented the methodological approach as transparently as possible and provided data (eg, appropriate citations in the paper and systematization of the statements in ) to substantiate the results. In our study, we focused on the German health care sector. Therefore, we only interviewed German experts. This might have an impact on the results for two reasons. First, some of the improvements identified could be specific to the German health care system. While, from our view, only a few improvements are affected by this, one should always scrutinize whether the individual identified improvements are transferable to other health care systems. Second, in the context of this publication, it was necessary to translate the individual statements into English. To avoid translation errors, we conducted quality checks of the citations within the consortium to guarantee that the initial meaning was not distorted.
Rather than providing an exhaustive list of all possible improvements, the objective of this study was to offer comprehensive perspectives. Therefore, it is unlikely that data saturation was fully achieved for each expert group. More specialized research should focus on specific groups, such as caregivers, patients, medical product consultants, experts in ethics and law, or manufacturers of AI-based CDSS, to identify conclusive lists of group-specific problems and improvements. Furthermore, the present findings should be further investigated and elaborated on in more detailed studies focusing on individual improvement categories or individual starting points for improvements, as well as in quantitative studies. A further practical step would be to systematically map implementation studies from the existing literature and examine them for successful improvement strategies.
Because AI-based CDSS are relatively new, there is no conclusive knowledge about underlying problems and barriers. Consequently, new problems and barriers could occur and require further solutions. Therefore, both the problems and barriers, as well as the improvements, should be continuously investigated and developed.
Conclusions
There is evidence that AI-based CDSS have the potential to improve patient outcomes and optimize efficiency in health care. However, to fully realize these benefits and to guarantee safety for patients and a good user experience for medical staff, it is essential to urgently address the existing barriers and unresolved challenges. Possible strategies for improvement should address the systems, the users, and the environment in which they are used. Experts saw concrete points of contact in “technology,” “data,” “user,” “studies,” “law,” and “general.” These should serve as a starting point for further, detailed research.
| 2022-12-01T00:00:00 |
https://medinform.jmir.org/2025/1/e69688
|
[
{
"date": "2022/12/01",
"position": 76,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 73,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 76,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 76,
"query": "artificial intelligence healthcare"
}
] |
|
Upcoming Utilization Review Restrictions in Light of Emerging ...
|
Upcoming Utilization Review Restrictions in Light of Emerging Artificial Intelligence
|
https://www.hklaw.com
|
[
"Jennifer Rangel",
"Danielle A. Giaccio"
] |
The impending limitations on AI in the Texas insurance landscape will impact how healthcare providers and insurers approach the UR process ...
|
With the rapid development of artificial intelligence (AI), state legislatures have been attempting to keep up with the technological advancing surge. Most recently, Texas enacted Senate Bill (SB) 815, a law imposing greater restrictions on the use of AI in utilization review (UR) and coverage determinations. This is not the first state to address the use of AI in UR and the insurance landscape, as California Gov. Gavin Newsom approved a similar bill on Sept. 28, 2024. In fact, it had been expected that the California law would set national precedence on AI-based regulations on insurers for the expedition and efficacy of coverage determinations, with additional information previously reported by Holland & Knight. The impending limitations on AI in the Texas insurance landscape will impact how healthcare providers and insurers approach the UR process statewide as the law takes effect Jan. 1, 2026.
AI Software Implications and Use in the UR Process
Insurers have begun to look to AI-based algorithms to expedite the medical necessity determination, which is the part of the UR process through which health plans consider and approve or deny coverage for particular patient services. However, Texas, like California and other states, has recognized the significance of continued human review.
The newly amended Texas law provides that a UR agent may not use an automated decision system to make an adverse determination either in whole or in part. An adverse determination includes a determination that healthcare services provided or proposed to be provided to a patient are not medically necessary or appropriate or are experimental or investigational.
Nonetheless, the law does not prohibit the use of an algorithm, AI system or automated decision system for administrative support or fraud detection functions. Instead, the amendment defines an AI system as any machine learning-based system that infers from the input received how to generate content, decisions, predictions and recommendations that can influence physical or virtual environments. An automated decision system means an algorithm including one using AI that uses data-based analytics to make, suggest or recommend certain determinations, decisions, judgments or conclusions.
Importantly, the notice provided to the insured regarding an adverse determination must include a description of the source of the screening criteria and review procedures used to make the adverse determination. In addition, the use of AI in UR may be audited or inspected at any time to ensure compliance with the stringent regulatory standards set forth under SB 815.
Looking Ahead
Similar to the predicted ramifications of the California law, only time will tell if integrating AI into the UR process will prove beneficial. Though AI is typically sought to streamline and expedite decisions, considering the limitations imposed by the regulations, it is possible that the impact of AI will be lessened as adverse determinations will require review procedures beyond AI-based algorithms and technologies.
It is important that AI be used in UR decisions only for administrative support or fraud and abuse detection with human oversight and review and that its use be disclosed in the notice to the insured. For insurers planning to utilize AI for UR in Texas, there will likely be strong oversight and potential review of adverse determinations made with AI to ensure that the final determination is not the flawed result of a technological error. It is likely that other states will follow Texas and California in enacting AI-focused regulations in the near future.
Stay tuned for updates as the potential capabilities and flaws of generative AI for insurance coverage review continue to develop upon this evolving regulatory landscape. For more information or questions, please contact the authors.
| 2022-12-01T00:00:00 |
https://www.hklaw.com/en/insights/publications/2025/07/upcoming-utilization-review-restrictions-in-light-of-emerging
|
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{
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{
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"date": "2025/07/01",
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|
Hippocratic AI
|
Hippocratic AI
|
https://www.hippocraticai.com
|
[] |
Safety Focused Generative AI for Healthcare · Choose an AI Agent to Get Started · Hear our GenAI Healthcare Agents in Action · See what healthcare leaders are ...
|
See what healthcare leaders are saying about us
| 2022-12-01T00:00:00 |
https://www.hippocraticai.com/
|
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{
"date": "2024/11/01",
"position": 37,
"query": "AI healthcare"
},
{
"date": "2024/12/01",
"position": 71,
"query": "AI employers"
},
{
"date": "2025/01/01",
"position": 48,
"query": "AI employers"
},
{
"date": "2025/01/01",
"position": 81,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 56,
"query": "AI healthcare"
},
{
"date": "2025/02/01",
"position": 45,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 47,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 66,
"query": "AI employers"
},
{
"date": "2025/04/01",
"position": 80,
"query": "AI healthcare"
},
{
"date": "2025/04/01",
"position": 47,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/06/01",
"position": 57,
"query": "AI healthcare"
}
] |
|
Free Online Certificate in Artificial Intelligence and Career ...
|
Free Online Certificate in Artificial Intelligence and Career Empowerment
|
https://www.rhsmith.umd.edu
|
[] |
Offered by the Center for Artificial Intelligence in Business and the ... AI adoption in fields such as marketing, finance, healthcare, and technology.
|
Program Description
This transformative course is designed for early to mid-career professionals looking to move to the private sector, or for a change in direction. The course provides an overview of Artificial Intelligence, how it is transforming functional areas in the private sector and what career opportunities may emerge during this time. Participants will explore how AI is transforming various industries, examining real-world applications and the broader implications of AI adoption in fields such as marketing, finance, healthcare, and technology. Through lectures from professors and interviews with industry experts, they will gain a deeper understanding of how AI is driving innovation, shaping job markets, and creating new opportunities across different sectors.
Given the global uncertainties in economies and the labor market, we also provide useful pointers for career empowerment, including how to navigate job opportunities, explore startups, negotiate well during challenging times and position yourself for AI-related opportunities in various sectors. This course blends theoretical knowledge with practical insights, ensuring participants not only understand AI concepts but also learn how to apply them effectively in their careers.
Participants will receive a completely free certificate in “Artificial Intelligence and Career Empowerment” from the Robert H. Smith School of Business at the University of Maryland.
| 2022-12-01T00:00:00 |
https://www.rhsmith.umd.edu/programs/executive-education/learning-opportunities-individuals/free-online-certificate-artificial-intelligence-and-career-empowerment
|
[
{
"date": "2022/12/01",
"position": 79,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 78,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 86,
"query": "AI healthcare"
},
{
"date": "2023/02/01",
"position": 78,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 85,
"query": "AI healthcare"
},
{
"date": "2023/02/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 81,
"query": "AI impact jobs"
},
{
"date": "2023/03/01",
"position": 78,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 69,
"query": "machine learning job market"
},
{
"date": "2023/07/01",
"position": 85,
"query": "AI healthcare"
},
{
"date": "2023/07/01",
"position": 88,
"query": "AI impact jobs"
},
{
"date": "2023/07/01",
"position": 73,
"query": "machine learning job market"
},
{
"date": "2023/08/01",
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"query": "AI healthcare"
},
{
"date": "2023/08/01",
"position": 87,
"query": "AI impact jobs"
},
{
"date": "2023/09/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/10/01",
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"query": "machine learning job market"
},
{
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"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 85,
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},
{
"date": "2023/11/01",
"position": 76,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 84,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 68,
"query": "machine learning job market"
},
{
"date": "2024/01/01",
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},
{
"date": "2024/02/01",
"position": 80,
"query": "AI impact jobs"
},
{
"date": "2024/02/01",
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"query": "artificial intelligence healthcare"
},
{
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},
{
"date": "2024/03/01",
"position": 75,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 68,
"query": "machine learning job market"
},
{
"date": "2024/04/01",
"position": 79,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 83,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 78,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/07/01",
"position": 70,
"query": "machine learning job market"
},
{
"date": "2024/09/01",
"position": 81,
"query": "AI healthcare"
},
{
"date": "2024/09/01",
"position": 82,
"query": "AI impact jobs"
},
{
"date": "2024/10/01",
"position": 81,
"query": "AI impact jobs"
},
{
"date": "2024/10/01",
"position": 92,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 77,
"query": "AI healthcare"
},
{
"date": "2024/11/01",
"position": 80,
"query": "AI impact jobs"
},
{
"date": "2024/11/01",
"position": 80,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 65,
"query": "machine learning job market"
},
{
"date": "2024/12/01",
"position": 80,
"query": "artificial intelligence education"
},
{
"date": "2025/01/01",
"position": 77,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 88,
"query": "generative AI jobs"
},
{
"date": "2025/01/01",
"position": 67,
"query": "machine learning job market"
},
{
"date": "2025/02/01",
"position": 85,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 83,
"query": "machine learning job market"
},
{
"date": "2025/03/01",
"position": 85,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 69,
"query": "machine learning job market"
},
{
"date": "2025/04/04",
"position": 49,
"query": "AI business leaders"
},
{
"date": "2025/04/04",
"position": 74,
"query": "artificial intelligence business leaders"
},
{
"date": "2025/04/01",
"position": 87,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 81,
"query": "machine learning job market"
},
{
"date": "2025/05/01",
"position": 94,
"query": "AI impact jobs"
},
{
"date": "2025/05/01",
"position": 81,
"query": "machine learning job market"
},
{
"date": "2025/06/01",
"position": 83,
"query": "machine learning job market"
}
] |
|
Healthcare Myths for Clinicians: 4 Assumptions to Rethink
|
Healthcare Myths for Clinicians: 4 Assumptions to Rethink
|
https://telehealth.org
|
[
"Tim Zenger",
"Mollie R. Cummins",
"Phd",
"Julia Ivanova",
"Jessica Stone",
"Ph.D.",
"Marlene M. Maheu",
"Aditi U. Joshi Md",
"Msc"
] |
Myth #1: AI Will Revolutionize Clinical Care Soon. Artificial intelligence (AI) is arguably the most hyped technology in healthcare today.
|
In an industry as complex and evolving as healthcare, specific themes dominate conversations: artificial intelligence, consolidation, value-based care, and rising insurance costs. These aren’t inherently transformative, but they’ve become focal points where transformation is urgently needed. Policymakers, industry leaders, and investors are increasingly pointing to these areas as leverage points for addressing a system strained by inefficiencies, cost burdens, and fragmentation.
But when you move beyond headlines and into actual operations, whether at the system, practice, or patient level, the impact of these trends is far-reaching. Many ideas driving policy, investment, and strategy are based on assumptions that don’t hold up under scrutiny.
This article dispels four widespread myths about healthcare strategy and innovation, examining what’s happening behind the scenes.
Myth #1: AI Will Revolutionize Clinical Care Soon
Artificial intelligence (AI) is arguably the most hyped technology in healthcare today. From generative documentation tools to predictive analytics, AI is pitched as the long-awaited fix to rising costs, clinician burnout, and documentation overload.
Spending projections reflect this optimism: CB Insights forecasts over $13 billion will be invested in AI agents in 2025. Additionally, a 2024 report by Bessemer Venture Partners, Bain & Company, and AWS showed that more than 80% of healthcare leaders believe AI will transform clinical decision-making.
However, less than 30% of AI pilot programs are successfully scaled into production. The reasons? Workflow misalignment, lack of trust in AI-generated outputs, data silos, regulatory uncertainty, and technical integration challenges. Many tools are still at the “demo stage”—appealing in theory but clunky in practice.
For instance, ambient AI scribes have garnered attention for their ability to automate note-taking. A recent JAMA Network Open study evaluated Nuance’s DAX Copilot at the University of Pennsylvania. While clinicians saw a 20% decrease in time spent on notes and a 30% reduction in after-hours work, they also reported the need for significant editing. Net promoter scores were neutral, and some clinicians found that the need for proofreading offset the time saved.
AI’s benefits are real, but uneven, and often overstated. Losses in accuracy and trust may offset gains in documentation speed.
Key Takeaway
AI won’t revolutionize care unless its implementation is designed for real-world workflows. It’s a tool, not a turnkey solution, and it requires governance, human oversight, and organizational alignment to deliver consistent value.
Myth #2: Bigger Health Systems Deliver Better Care
Consolidation has reshaped the U.S. healthcare landscape. Large hospital systems, private equity-backed groups, and national care delivery organizations have acquired independent clinics at record rates. The theory is that scale enables efficiency, technology adoption, and improved access.
But for many patients and frontline professionals, the day-to-day experience tells a different story:
Longer wait times for basic care .
. More steps in authorization and billing .
. Reduced flexibility and autonomy for clinicians .
. Opaque pricing and surprise billing.
Despite access to massive data sets, many large systems still struggle to translate information into insights or coordinated action. Decision-making becomes slower, complexity increases, and agility is lost.
At the same time, clinicians report feeling like cogs in a machine, disconnected from leadership decisions and overloaded by administrative demands. The push for standardization often results in generic, inflexible care models that don’t reflect local needs or patient preferences.
Bigger isn’t better unless size is paired with responsiveness and accountability. Too often, system expansion scales inefficiencies.
Key Takeaway
Size should serve outcomes, not just operational footprint. Leaders must invest in simplifying internal processes and aligning growth with mission, not just margin.
Myth #3: Value-Based Care Is Already Delivering Results
Value-based care (VBC) has been a cornerstone of U.S. healthcare policy for over a decade. The goal is intuitive: shift financial incentives from volume to value, reward outcomes over services, and build accountability into payment models.
However, in practice, VBC implementation remains limited and frequently misunderstood.
Despite years of promotion, most provider contracts remain tied to fee-for-service (FFS) reimbursement. A smaller proportion includes upside-only incentives, such as pay-for-performance bonuses. Still, very few organizations are engaged in full-risk, downside arrangements, where they would be penalized financially for poor outcomes or high costs. (4)
Why the resistance? Risk is difficult to quantify, and most providers operate in highly variable environments. Infrastructure, data systems, and actuarial support are often insufficient to take on downside financial exposure without jeopardizing operations.
In a national study I led, we interviewed nearly 100 population health executives and asked each to define “population health.” Despite shared goals, 87% gave unique answers, underscoring a fundamental misalignment in terms, measures, and operating strategies. Without a shared language, even good ideas can’t scale effectively.
Meanwhile, federal efforts have produced disappointing results. CMMI’s recent portfolio evaluation found over $6 billion in net model losses across 52 payment experiments, with only a handful generating meaningful or sustained savings.
As one respondent told me, “If you’ve seen one value-based model… you’ve seen one.” Variation in design, execution, and measurement has limited replicability.
Key Takeaway
VBC is still in its early phases. To fulfill its potential, the industry needs better infrastructure, more standardized models, and gradual adoption of shared risk, especially downside risk, as a norm, not an exception.
Myth #4: Insurance Is the Only Path to Affordable Access
For decades, traditional insurance has been positioned as the gatekeeper to care. However, the economics of that model are increasingly breaking down for both patients and providers.
In 2025, the average U.S. family will spend over $20,000 annually on healthcare when combining personal and employer premium contributions before any services are even rendered. Meanwhile, administrative waste continues to climb, with prior authorizations, coding audits, and reimbursement disputes adding layers of inefficiency.
Clinicians report that dealing with insurance is one of the most time-consuming and demoralizing parts of practice. It often means restricted networks, delayed care, and unclear patient billing.
This landscape fuels interest in direct-to-consumer (DTC) models offering transparency, simplicity, and control. Care is priced upfront in these models, and administrative drag is minimal.
The AMA notes that prior authorization is one of the top drivers of physician interest in direct care models, particularly in primary and outpatient specialties.
While these models aren’t universal solutions, they demonstrate that insurance isn’t the only viable payment pathway. DTC models may offer greater alignment between cost, quality, and experience for outpatient, preventive, and behavioral services.
Key Takeaway
Insurance may remain dominant, but it no longer defines the boundaries of access to care. DTC and hybrid models represent a growing alternative that can reduce overhead and improve patient satisfaction.
Conclusion: Challenging the Narrative to Find the Real Opportunity
Healthcare transformation and AI in healthcare are often discussed in abstract, optimistic terms. But when we step back from the noise, it’s clear that many of the biggest ideas—AI, consolidation, value-based care, and insurance reform—are still in development and not yet delivering on their promises.
That doesn’t mean they should be dismissed. On the contrary, each trend contains enormous potential—but only if approached with clarity, critical thinking, and a willingness to challenge assumptions.
Whether you’re leading a health system, designing policy, managing a growth-stage health tech company, or are an individual provider, the question isn’t “What’s trending?”—it’s “What’s working?”
Sustainable improvement in healthcare will come from thoughtful design, realistic expectations, and execution that respects the system’s complexity, not from doubling down on half-built ideas.
Therapist AI & ChatGPT: How to Use Legally & Ethically Immerse yourself in our highly-engaging eLearning program and delve into the uncharted territory of Artificial Intelligence (AI) in Behavioral Healthcare! Read More
AI Literacy: Ethical & Practical Behavioral Health Applications This artificial intelligence (AI) learning experience provides behavioral health professionals with a foundational understanding of its emerging role. It covers AI fundamentals, ethical considerations, practical applications in therapy and diagnostics, and the future of AI in enhancing client and patient outcomes. Through interactive modules, case studies, and expert insights, participants will gain the skills to critically evaluate AI tools and responsibly integrate them into their practice. Read More
| 2025-07-08T00:00:00 |
2025/07/08
|
https://telehealth.org/blog/rethinking-healthcare-4-myths-clinicians-and-leaders-should-know/
|
[
{
"date": "2022/12/01",
"position": 80,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
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},
{
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},
{
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},
{
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
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"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 87,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 86,
"query": "artificial intelligence healthcare"
}
] |
AI Risk Management Framework | NIST
|
AI Risk Management Framework
|
https://www.nist.gov
|
[] |
NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI).
|
Quick Links
Download the AI RMF 1.0 View the AI RMF Playbook Visit the AI Resource Center
Overview of the AI RMF
In collaboration with the private and public sectors, NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.
Released on January 26, 2023, the Framework was developed through a consensus-driven, open, transparent, and collaborative process that included a Request for Information, several draft versions for public comments, multiple workshops, and other opportunities to provide input. It is intended to build on, align with, and support AI risk management efforts by others (Fact Sheet).
A companion NIST AI RMF Playbook also has been published by NIST along with an AI RMF Roadmap, AI RMF Crosswalk, and various Perspectives.
On March 30, 2023, NIST launched the Trustworthy and Responsible AI Resource Center, which will facilitate implementation of, and international alignment with, the AI RMF. Examples of how other organizations are building on and using the AI RMF can be found via the AIRC’s Use Case page.
On July 26, 2024, NIST released NIST-AI-600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. The profile can help organizations identify unique risks posed by generative AI and proposes actions for generative AI risk management that best aligns with their goals and priorities.
To view public comments received on the previous drafts of the AI RMF and Requests for Information, see the AI RMF Development page.
Prior Documents
| 2021-07-12T00:00:00 |
2021/07/12
|
https://www.nist.gov/itl/ai-risk-management-framework
|
[
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"query": "artificial intelligence healthcare"
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{
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},
{
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"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
},
{
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"position": 84,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 82,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/02/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/03/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/04/01",
"position": 96,
"query": "artificial intelligence healthcare"
}
] |
Abridge: Generative AI for Clinical Conversations
|
Generative AI for Clinical Conversations
|
https://www.abridge.com
|
[] |
Discover how Abridge transforms documentation for clinical conversations powered by generative AI, enhancing healthcare understanding and improving patient ...
|
Integrated Directly Inside Epic
From Haiku to Hyperdrive, harness the power of Abridge from start to finish without ever leaving Epic.
| 2022-12-01T00:00:00 |
https://www.abridge.com/
|
[
{
"date": "2022/12/01",
"position": 85,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
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"query": "AI healthcare"
},
{
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|
KPMG and Hippocratic AI announce collaboration to transform ...
|
KPMG and Hippocratic AI announce collaboration to transform healthcare delivery using AI healthcare agents to tackle global sector staffing shortages
|
https://kpmg.com
|
[
"Munjal Shah",
"Founder",
"Ceo Of Hippocratic Ai"
] |
... healthcare workforce shortage that endangers the continuity of care. Hippocratic AI's generative AI healthcare agents can address such risks ...
|
London, UK – July 8, 2025 – Today KPMG International announces its innovative and timely work with healthcare technology company Hippocratic AI, global leader in developing safety-first generative AI healthcare agents that work alongside healthcare workers, performing non-diagnostic clinical tasks for patients.
The international healthcare sector is experiencing a significant workforce shortage, with projections indicating a shortfall of approximately 10 million health workers by 2030. To help address this and relieve backlogs in healthcare systems worldwide, Hippocratic AI’s generative AI agents safely conduct non-diagnostic patient-facing clinical tasks marking a major milestone in addressing the global workforce shortage. KPMG’s global healthcare professionals are collaborating with Hippocratic AI to reimagine care delivery to support today’s constrained healthcare workforce and create more efficient operational processes, ultimately alleviating overload on the workforce and improving patient outcomes. More specifically, KPMG is conducting broad process analyses to identify high-pressure points and upskill workforces to help best augment the workforce with AI and strategically plan for the highest impact deployment of AI across the entire care continuum.
Hippocratic’s generative AI agents can free up provider time to focus on their patients using conversational agents designed to interact with humans in a natural intuitive way as the agents comprehend, process, and respond to human conversation in a contextually relevant and human-like manner. Hippocratic AI’s work represents a paradigm shift in how care is administered, signifying a move toward more abundant healthcare. Powered by its patented Polaris Constellation architecture, which features specialized large language support models, the generative AI healthcare agents can deliver a range of healthcare workflows from patient intake to care management follow up calls. KPMG firms support by conducting broad process analyses to identify high-pressure points and upskill workforces to ensure human-AI alignment. This exercise enables KPMG firms to strategically plan the deployment of AI across the entire care continuum, effectively managing potential disruptions to achieve maximal value in productivity and patient outcomes.
“Hippocratic AI's collaboration with KPMG is deeply aligned in purpose and vision. Their holistic approach to digital and clinical transformation focuses on improving patient outcomes and optimizing healthcare efficiency. We appreciate their commitment to driving meaningful impact across the entire care journey with generative AI, while preserving the human touch of clinicians and the integrity of healthcare operations,” said Munjal Shah, Founder and CEO of Hippocratic AI.
“As societies age, we are facing a critical healthcare workforce shortage that endangers the continuity of care. Hippocratic AI’s generative AI healthcare agents can address such risks, however, to unlock their full value, a coherent and robust approach is needed to transform operational processes and upskill and empower clinical staff so the human workforce and their AI agent colleagues can operate in concert,” said Dr. Anna van Poucke, KPMG Global Healthcare Leader.
| 2022-12-01T00:00:00 |
https://kpmg.com/xx/en/media/press-releases/2025/07/kpmg-and-hippocratic-ai-announce-collaboration-to-transform-healthcare.html
|
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|
AI leads the way: 9 megadeals defining digital health in '25
|
AI leads the way: 9 megadeals defining digital health in '25 - Becker's Hospital Review
|
https://www.beckershospitalreview.com
|
[
"Giles Bruce",
"Naomi Diaz",
"Tuesday",
"July",
"Dr. Mark Pratt",
"Chief Medical Officer",
"Altera Digital Health",
"Hours Ago",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class"
] |
Regional Healthcare System plans to go live with Epic on July 9. The one-hospital system is switching to… By: Giles Bruce. 6 hours ago ...
|
For the first time, AI-based startups now account for the majority of digital health venture capital funding, Rock Health reported.
AI-enabled companies made up 62% of digital health venture funding in the first half of 2025, according to the digital health group’s July 7 report. Here are the biggest deals involving AI-based startups from the first six months of the year:
1. Truveta: $320 million (series C)
2. Abridge: $300 million (series E)
3. Innovaccer: $275 million (series F)
4. Abridge: $250 million (series D)
5. Commure: $200 million (growth)
6. Hippocratic AI: $141 million (series B)
7. Persivia: $107 million (growth)
8. Qventus: $105 million (series D)
9. Tennr: $101 million (series C)
| 2025-07-08T00:00:00 |
2025/07/08
|
https://www.beckershospitalreview.com/healthcare-information-technology/ai/ai-leads-the-way-9-megadeals-defining-digital-health-in-25/
|
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"query": "artificial intelligence healthcare"
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}
] |
RACGP releases new AI guidance
|
RACGP releases new AI guidance
|
https://www1.racgp.org.au
|
[] |
... AI tools. Artificial intelligence (AI) is becoming increasingly relevant in healthcare, but at least 80% of GPs have reported that they are ...
|
News
RACGP releases new AI guidance
Anastasia Tsirtsakis 7/07/2025 3:42:18 PM A new resource guides GPs through the practicalities of using conversational AI in their consults, how the new technology works, and risks to be aware of.
AI is an emerging space in general practice, with more than half of GPs not familiar with specific AI tools.
Artificial intelligence (AI) is becoming increasingly relevant in healthcare, but at least 80% of GPs have reported that they are not at all, or not very, familiar with specific AI tools.
To help GPs broaden their understanding of the technology, and weigh up the potential advantages and disadvantages of its use in their practice, the RACGP has unveiled a comprehensive new resource focused on conversational AI.
Unlike AI scribes, which convert a conversation with a patient into a clinical note that can be incorporated into a patient’s health record, conversational AI is technology that enables machines to interpret, process, and respond to human language in a natural way.
Examples include AI-powered chatbots and virtual assistants that can support patient interactions, streamline appointment scheduling, and automate routine administrative tasks.
The college resource offers practical guidance on how conversational AI can be applied effectively in general practice and highlights key applications. These include:
answering patient questions regarding their diagnosis, potential side effects of prescribed medicines or by simplifying jargon in medical reports
providing treatment/medication reminders and dosage instructions
providing language translation services
guiding patients to appropriate resources
supporting patients to track and monitor blood pressure, blood sugar, or other health markers
triaging patients prior to a consultation
preparing medical documentation such as clinical letters, clinical notes and discharge summaries
providing clinical decision support by preparing lists of differential diagnoses, supporting diagnosis, and optimising clinical decision support tools (for investigation and treatment options)
suggesting treatment options and lifestyle recommendations.
Dr Rob Hosking, Chair of the RACGP’s Practice and Technology Management Expert Committee, told newsGP there are several potential advantages to these tools in general practice.‘Some of the potential benefits include task automation, reduced administrative burden, improved access to care and personalised health education for patients,’ he said.Beyond the clinical setting, conversational AI tools can also have a range of business, educational and research applications, such as automating billing and analysing billing data, summarising the medical literature and answering clinicians’ medical questions.However, while there are a number of benefits, Dr Hosking says it is important to consider some of the potential disadvantages to its use as well.‘Conversational AI tools can provide responses that appear authoritative but on review are vague, misleading, or even incorrect,’ he explained.‘Biases are inherent to the data on which AI tools are trained, and as such, particular patient groups are likely to be underrepresented in the data.‘There is a risk that conversational AI will make unsuitable and even discriminatory recommendations, rely on harmful and inaccurate stereotypes, and/or exclude or stigmatise already marginalised and vulnerable individuals.’While some conversational AI tools are designed for medical use, such as Google’s MedPaLM and Microsoft’s BioGPT, Dr Hosking pointed out that most are designed for general applications and not trained to produce a result within a clinical context.‘The data these general tools are trained on are not necessarily up-to-date or from high-quality sources, such as medical research,’ he said.The college addresses these potential problems, as well as other ethical and privacy concerns, that come with using AI in healthcare.For GPs deciding whether to use conversational AI, Dr Hosking notes that there are a number of considerations to ensure the delivery of safe and quality care, and says that patients should play a key role in the decision-making process as to whether to use it in their specific consultation.‘GPs should involve patients in the decision to use AI tools and obtain informed patient consent when using patient-facing AI tools,’ he said.‘Also, do not input sensitive or identifying data.’However, before conversational AI is brought into practice workflows, the RACGP recommends GPs are trained on how to use it safely, including knowledge around the risks and limitations of the tool, and how and where data is stored.‘GPs must ensure that the use of the conversational AI tool complies with relevant legislation and regulations, as well as any practice policies and professional indemnity insurance requirements that might impact, prohibit or govern its use,’ the college resource states.‘It is also worth considering that conversational AI tools designed specifically by, and for use by, medical practitioners are likely to provide more accurate and reliable information than that of general, open-use tools.‘These tools should be TGA-registered as medical devices if they make diagnostic or treatment recommendations.’While the college recognises that conversational AI could revolutionise parts of healthcare delivery, in the interim, it recommends that GPs be ‘extremely careful’ in using the technology at this time.‘Many questions remain about patient safety, patient privacy, data security, and impacts for clinical outcomes,’ the college said.Dr Hosking, who has yet to implement conversational AI tools in his own clinical practice, shared the sentiment.‘AI will continue to evolve and really could make a huge difference in patient outcomes and time savings for GPs,’ he said.‘But it will never replace the important role of the doctor-patient relationship. We need to ensure AI does not create health inequities through inbuilt biases.‘This will help GPs weigh up the potential advantages and disadvantages of using conversational AI in their practice and inform of the risks associated with these tools.’Log in below to join the conversation.
AI AI scribes artificial intelligence conversational AI
| 2022-12-01T00:00:00 |
https://www1.racgp.org.au/newsgp/professional/racgp-releases-new-ai-guidance?feed=RACGPnewsGPArticles
|
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{
"date": "2022/12/01",
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|
Implementing Artificial Intelligence in Critical Care Medicine
|
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22 - Critical Care
|
https://ccforum.biomedcentral.com
|
[
"Cecconi",
"Humanitas University",
"Milan",
"Irccs Humanitas Research Hospital",
"Greco",
"Shickel",
"Intelligent Clinical Care Center",
"University Of Florida",
"Gainesville",
"Department Of Medicine"
] |
A social contract for AI in healthcare should define clear roles and responsibilities for all stakeholders—clinicians, patients, developers, ...
|
Artificial intelligence (AI) is rapidly entering critical care, where it holds the potential to improve diagnostic accuracy and prognostication, streamline intensive care unit (ICU) workflows, and enable personalized care. [1, 2] Without a structured approach to implementation, evaluation, and control, this transformation may be hindered or possibly lead to patient harm and unintended consequences.
Despite the need to support overwhelmed ICUs facing staff shortages, increasing case complexity, and rising costs, most AI tools remain poorly validated and untested in real settings. [3, 45]
To address this gap, we issue a call to action for the critical care community: the integration of AI into the ICU must follow a pragmatic, clinically informed, and risk-aware framework. [6,7,8] As a result of a multidisciplinary consensus process with a panel of intensivists, AI researchers, data scientists and experts, this paper offers concrete recommendations to guide the safe, effective, and meaningful adoption of AI into critical care.
Methods
The consensus presented in this manuscript emerged through expert discussions, rather than formal grading or voting on evidence, in recognition that AI in critical care is a rapidly evolving field where many critical questions remain unanswered. Participants were selected by the consensus chairs (MC, AB, FT, and JLV) based on their recognized contributions to AI in critical care to ensure representation from both clinical end-users and AI developers. Discussions were iterative with deliberate engagement across domains, refining recommendations through critical examination of real-world challenges, current research, and regulatory landscapes.
While not purely based on traditional evidence grading, this manuscript reflects a rigorous, expert-driven synthesis of key barriers and opportunities for AI in critical care, aiming to bridge existing knowledge gaps and provide actionable guidance in a rapidly evolving field. To guide physicians in this complex and rapidly evolving arena [9], some of the current taxonomy and classifications are reported in Fig. 1.
Fig. 1 Taxonomy of AI in critical care Full size image
Main barriers and challenges for AI integration in critical care
The main barriers to AI implementation in critical care determined by the expert consensus are presented in this section. These unresolved and evolving challenges have prompted us to develop a series of recommendations to physicians and other healthcare workers, patients, and societal stakeholders, emphasizing the principles we believe should guide the advancement of AI in healthcare. Challenges and principles are divided into four main areas, 1) human-centric AI; 2) Recommendation for clinician training on AI use; 3) standardization of data models and networks and 4) AI governance. These are summarized in Fig. 2 and discussed in more detail in the next paragraphs.
Fig. 2 Recommendations, according to development of standards for networking, data sharing and research, ethical challenges, regulations and societal challenges, and clinical practice Full size image
The development and maintenance of AI applications in medicine require enormous computational power, infrastructure, funding and technical expertise. Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11]. The rapid diffusion of new AI models contrasts sharply with the evidence-based culture of medicine. This raises concerns about the deployment of insufficiently validated clinical models. [12]
Moreover, many models are developed using datasets that underrepresent vulnerable populations, leading to algorithmic bias. [13] AI models may lack both temporal validity (when applied to new data in a different time) and geographic validity (when applied across different institutions or regions). Variability in temporal or geographical disease patterns including demographics, healthcare infrastructure, and the design of Electronic Health Records (EHR) further complicates generalizability.
Finally, the use of AI raises ethical concerns, including trust in algorithmic recommendations and the risk of weakening the human connection at the core of medical practice, which is the millenary relation between physicians and patients. [14]
Recommendations
Here we report recommendations, divided in four domains. Figure 3 reports a summary of five representative AI use cases in critical care—ranging from waveform analysis to personalized clinician training—mapped across these four domains.
Fig. 3 Summary of five representative AI use cases in critical care—ranging from waveform analysis to personalized clinician training—mapped across these 4 domains Full size image
Strive for human-centric and ethical AI utilization in healthcare
Alongside its significant potential benefit, the risk of AI misuse cannot be underestimated. AI algorithms may be harmful when prematurely deployed without adequate control [9, 15,16,17]. In addition to the regulatory frameworks that have been established to maintain control (presented in Sect."Governance and regulation for AI in Critical Care") [18, 19] we advocate for clinicians to be involved in this process and provide guidance.
Develop human-centric AI in healthcare
AI development in medicine and healthcare should maintain a human-centric perspective, promote empathetic care, and increase the time allocated to patient-physician communication and interaction. For example, the use of AI to replace humans in time-consuming or bureaucratic tasks such as documentation and transfers of care [20,21,22]. It could craft clinical notes, ensuring critical information is accurately captured in health records while reducing administrative burdens [23].
Establish social contract for AI use in healthcare
There is a significant concern that AI may exacerbate societal healthcare disparities [24]. When considering AI’s potential influence on physicians'choices and behaviour, the possibility of including or reinforcing biases should be examined rigorously to avoid perpetuating existing health inequities and unfair data-driven associations [24]. It is thus vital to involve patients and societal representatives in discussions regarding the vision of the next healthcare era, its operations, goals, and limits of action [25]. The desirable aim would be to establish a social contract for AI in healthcare, to ensure the accountability and transparency of AI in healthcare. A social contract for AI in healthcare should define clear roles and responsibilities for all stakeholders—clinicians, patients, developers, regulators, and administrators. This includes clinicians being equipped to critically evaluate AI tools, developers ensuring transparency, safety, and clinical relevance, and regulators enforcing performance, equity, and post-deployment monitoring standards. We advocate for hospitals to establish formal oversight mechanisms, such as dedicated AI committees, to ensure the safe implementation of AI systems. Such structures would help formalize shared accountability and ensure that AI deployment remains aligned with the core values of fairness, safety, and human-centred care.
Prioritize human oversight and ethical governance in clinical AI
Since the Hippocratic oath, patient care has been based on the doctor-patient connection where clinicians bear the ethical responsibility to maximize patient benefit while minimizing harm. As AI technologies are increasingly integrated into healthcare, their responsibility must also extend to overseeing its development and application. In the ICU, where treatment decisions balance between individual patient preferences and societal consideration, healthcare professionals must lead this transition [26]. As intensivists, we should maintain governance of this process, ensuring ethical principles and scientific rigor guide the development of frameworks to measure fairness, assess bias, and establish acceptable thresholds for AI uncertainty [6,7,8].
While AI models are rapidly emerging, most are being developed outside the medical community. To better align AI development with clinical ethics, we propose the incorporation of multidisciplinary boards comprising clinicians, patients, ethicists, and technological experts, who should be responsible for systematically reviewing algorithmic behaviour in critical care, assessing the risks of bias, and promoting transparency in decision-making processes. In this context, AI development offers an opportunity to rethink and advance ethical principles in patient care.
Recommendations for clinician training on AI use
Develop and assess the Human-AI interface
Despite some promising results [27, 28], the clinical application of AI remains limited [29,30,31]. The first step toward integration is to understand how clinicians interact with AI and to design systems that complement, rather than disrupt, clinical reasoning [32]. This translates into the need for specific research on the human-AI interface, where a key area of focus is identifying the most effective cognitive interface between clinicians and AI systems. On one side, physicians may place excessive trust on AI model results, possibly overlooking crucial information. For example, in sepsis detection an AI algorithm might miss an atypical presentation or a tropical infectious disease due to limitations in its training data; if clinicians overly trust the algorithm’s negative output, they may delay initiating a necessary antibiotic. On the other, the behaviour of clinicians can influence AI responses in unintended ways. To better reflect this interaction, the concept of synergy between human and AI has been proposed in the last years, emphasizing that AI supports rather than replaces human clinicians [33]. This collaboration has been described in two forms: human-AI augmentation (when human–AI interface enhances clinical performance compared to human alone) and human-AI synergy (where the combined performance exceeds that of both the human and the AI individually) [34]. To support the introduction of AI in clinical practice in intensive care, we propose starting with the concept of human-AI augmentation, which is more inclusive and better established according to medical literature [34]. A straightforward example of the latter is the development of interpretable, real-time dashboards that synthetize complex multidimensional data into visual formats, thereby enhancing clinicians’ situational awareness without overwhelming them.
Improve disease characterization with AI
Traditional procedures for classifying patients and labelling diseases and syndromes based on a few simple criteria are the basis of medical education, but they may fail to grasp the complexity of underlying pathology and lead to suboptimal care. In critical care, where patient conditions are complex and rapidly evolving, AI-driven phenotyping plays a crucial role by leveraging vast amounts of genetic, radiological, biomarker, and physiological data. AI-based phenotyping methods can be broadly categorized into two approaches.
One approach involves unsupervised clustering, in which patients are grouped based on shared features or patterns without prior labelling. Seymour et al. demonstrated how machine learning can stratify septic patients into clinically meaningful subgroups using high-dimensional data, which can subsequently inform risk assessment and prognosis [35]. Another promising possibility is the use of supervised or semi-supervised clustering techniques, which incorporate known outcomes or partial labelling to enhance the phenotyping of patient subgroups [36].
The second approach falls under the causal inference framework, where phenotyping is conducted with the specific objective of identifying subgroups that benefit from a particular intervention due to a causal association. This method aims to enhance personalized treatment by identifying how treatment effects vary among groups, ensuring that therapies are targeted toward patients most likely to benefit. For example, machine learning has been used to stratify critically ill patients based on their response to specific therapeutic interventions, potentially improving clinical outcomes [37]. In a large ICU cohort of patients with traumatic brain injury (TBI), unsupervised clustering identified six distinct subgroups, based on combined neurological and metabolic profiles. [38]
These approaches hold significant potential for advancing acute and critical care by ensuring that AI-driven phenotyping is not only descriptive, but also actionable. Before integrating these methodologies into clinical workflows, we need to make sure clinicians can accept the paradigm shift between broad syndromes and specific sub-phenotypes, ultimately supporting the transition toward personalized medicine [35, 39,40,41].
Ensure AI training for responsible use of AI in healthcare
In addition to clinical practice, undergraduate medical education is also directly influenced by AI transformation [42] as future workers need to be equipped to understand and use these technologies. Providing training and knowledge from the start of their education requires that all clinicians understand data science and AI's fundamental concepts, methods, and limitations, which should be included in medical degree core curriculum. This will allow clinicians to use and assess AI critically, identify biases and limitations, and make well-informed decisions, which may ultimately benefit the medical profession's identity crisis and provide new careers in data analysis and AI research [42].
In addition to undergraduate education, it is essential to train experienced physicians, nurses, and other allied health professional [43]. The effects of AI on academic education are deep and outside the scope of the current manuscript. One promising example is the use of AI to support personalized, AI-driven training for clinicians—both in clinical education and in understanding AI-related concepts [44]. Tools such as chatbots, adaptive simulation platforms, and intelligent tutoring systems can adapt content to students’ learning needs in real time, offering a tailored education. This may be applied to both clinical training and training in AI domains.
Accepting uncertainty in medical decision-making
Uncertainty is an intrinsic part of clinical decision-making, with which clinicians are familiar and are trained to navigate it through experience and intuition. However, AI models introduce a new type of uncertainty, which can undermine clinicians'trust, especially when models function as opaque “black boxes” [45,46,47]. This increases cognitive distance between model and clinical judgment, as clinicians don’t know how to interpret it. To bridge this gap, explainable AI (XAI) has emerged, providing tools to make model predictions more interpretable and, ideally, more trustworthy to reduce perceived uncertainty [48].
Yet, we argue that interpretability alone is not enough [48].To accelerate AI adoption and trust, we advocate that physicians must be trained to interpret outputs under uncertainty—using frameworks like plausibility, consistency with known biology, and alignment with consolidated clinical reasoning—rather than expecting full explainability [49].
Standardize and share data while maintaining patient privacy
In this section we present key infrastructures for AI deployment in critical care [50]. Their costs should be seen as investment in patient outcomes, processes efficiency, and reduced operational costs. Retaining data ownership within healthcare institutions, and recognizing patients and providers as stakeholders, allows them to benefit from the value their data creates. On the contrary, without safeguards clinical data risk becoming proprietary products of private companies—which are resold to their source institutions rather than serving as a resource for their own development—for instance, through the development and licensing of synthetic datasets [51].
Standardize data to promote reproducible AI models
Standardized data collection is essential for creating generalizable and reproducible AI models and fostering interoperability between different centres and systems. A key challenge in acute and critical care is the variability in data sources, including EHRs, multi-omics data (genomics, transcriptomics, proteomics, and metabolomics), medical imaging (radiology, pathology, and ultrasound), and unstructured free-text data from clinical notes and reports. These diverse data modalities are crucial for developing AI-driven decision-support tools, yet their integration is complex due to differences in structure, format, and quality across healthcare institutions.
For instance, the detection of organ dysfunction in the ICU, hemodynamic monitoring collected by different devices, respiratory parameters from ventilators by different manufacturers, and variations in local policies and regulations all impact EHR data quality, structure, and consistency across different centres and clinical trials.
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which embeds standard vocabularies such as LOINC and SNOMED CT, continues to gain popularity as a framework for structuring healthcare data, enabling cross-centre data exchange and model interoperability [52,53,54]. Similarly, Fast Healthcare Interoperability Resources (FHIR) offers a flexible, standardized information exchange solution, facilitating real-time accessibility of structured data [55].
Hospitals, device and EHR companies must contribute to the adoption of recognized standards to make sure interoperability is not a barrier to AI implementation.
Beyond structured data, AI has the potential to enhance data standardization by automatically tagging and labelling data sources, tracking provenance, and harmonizing data formats across institutions. Leveraging AI for these tasks can help mitigate data inconsistencies, thereby improving the reliability and scalability of AI-driven clinical applications.
Prioritize data safety, security, and patient privacy
Data safety, security and privacy are all needed for the application of AI in critical care. Data safety refers to the protection of data from accidental loss or system failure, while data security is related with defensive strategies for malicious attacks including hacking, ransomware, or unauthorized data access [56]. In modern hospitals, data safety and security will soon become as essential as wall oxygen in operating rooms [57, 58]. A corrupted or hacked clinical dataset during hospital care could be as catastrophic as losing electricity, medications, or oxygen. Finally, data privacy focuses on the safeguard of personally information, ensuring that patient data is stored and accessed in compliance with legal standards [56].
Implementing AI that prioritizes these three pillars will be critical for resilient digital infrastructure in healthcare. A possible option for the medical community is to support open-source modes to increase transparency and reduce dependence on proprietary algorithms, and possibly enable better control of safety and privacy issues within the distributed systems [59]. However, sustaining open-source innovation requires appropriate incentives, such as public or dedicated research funding, academic recognition, and regulatory support to ensure high-quality development and long-term viability [60]. Without such strategies, the role of open-source models will be reduced, with the risk of ceding a larger part of control of clinical decision-making to commercial algorithms.
Develop rigorous AI research methodology
We believe AI research should be held to the same methodological standards of other areas of medical research. Achieving this will require greater accountability from peer reviewers and scientific journals to ensure rigor, transparency, and clinical relevance.
Furthermore, advancing AI in ICU research requires a transformation in the necessary underlying infrastructure, particularly when considering high-frequency data collection and the integration of complex, multimodal patient information, detailed in the sections below. In this context, the gap in data resolution between highly monitored environments such as ICUs and standard wards become apparent. The ICU provides a high level of data granularity due to high resolution monitoring systems, capable of capturing the rapid changes in a patient's physiological status [61]. Consequently, the integration of this new source of high-volume, rapidly changing physiological data into medical research and clinical practice could give rise to “physiolomics”, a proposed term to describe this domain, that could become as crucial as genomics, proteomics and other “-omics” fields in advancing personalized medicine.
AI will change how clinical research is performed, improving evidence-based medicine and conducting randomized clinical trials (RCTs) [62]. Instead of using large, heterogeneous trial populations, AI might help researchers design and enrol tailored patient subgroups for precise RCTs [63, 64]. These precision methods could solve the problem of negative critical care trials related to inhomogeneities in the population and significant confounding effects. AI could thus improve RCTs by allowing the enrolment of very subtle subgroups of patients with hundreds of specific inclusion criteria over dozens of centres, a task impossible to perform by humans in real-time practice, improving trial efficiency in enrolling enriched populations [65,66,67]. In the TBI example cited, conducting an RCT on the six AI-identified endotypes—such as patients with moderate GCS but severe metabolic derangement—would be unfeasible without AI stratification [38]. This underscores AI’s potential to enable precision trial designs in critical care.
There are multiple domains for interaction between AI and RCT, though a comprehensive review is beyond the scope of this paper. These include trial emulation to identify patient populations that may benefit most from an intervention, screening for the most promising drugs for interventions, detecting heterogeneity of treatment effects, and automated screening to improve the efficiency and cost of clinical trials.
Ensuring that AI models are clinically effective, reproducible, and generalizable requires adherence to rigorous methodological standards, particularly in critical care where patient heterogeneity, real-time decision-making, and high-frequency data collection pose unique challenges. Several established reporting and validation frameworks already provide guidance for improving AI research in ICU settings. While these frameworks are not specific to the ICU environment, we believe these should be rapidly disseminated into the critical care community through dedicated initiatives, courses and scientific societies.
For predictive models, the TRIPOD-AI extension of the TRIPOD guidelines focuses on transparent reporting for clinical prediction models with specific emphasis on calibration, internal and external validation, and fairness [68]. PROBAST-AI framework complements this by offering a structured tool to assess risk of bias and applicability in prediction model studies [69]. CONSORT-AI extends the CONSORT framework to include AI-specific elements such as algorithm transparency and reproducibility for interventional trials with AI [70], while STARD-AI provides a framework for reporting AI-based diagnostic accuracy studies [71]. Together, these guidelines encompass several issues related to transparency, reproducibility, fairness, external validation, and human oversight—principles that must be considered foundational for any trustworthy AI research in healthcare. Despite the availability of these frameworks, many ICU studies involving AI methods still fail to meet these standards, leading to concerns about inadequate external validation and generalizability [68, 72, 73].
Beyond prediction models, critical care-specific guidelines proposed in recent literature offer targeted recommendations for evaluating AI tools in ICU environments, particularly regarding data heterogeneity, patient safety, and integration with clinical workflows. Moving forward, AI research in critical care must align with these established frameworks and adopt higher methodological standards, such as pre-registered AI trials, prospective validation in diverse ICU populations, and standardized benchmarks for algorithmic performance.
Encourage collaborative AI models
Centralizing data collection from multiple ICUs, or federating them into structured networks, enhances external validity and reliability by enabling a scale of data volume that would be unattainable for individual institutions alone [74]. ICUs are at the forefront of data sharing efforts, offering several publicly available datasets for use by the research community [75]. There are several strategies to build collaborative databases. Networking refers to collaborative research consortia [76] that align protocols and pool clinical research data across institutions. Federated learning, by contrast, involves a decentralized approach where data are stored locally and only models or weights are shared between centres [77]. Finally, centralized approaches, such as the Epic Cosmos initiative, leverage de-identified data collected from EHR and stored on a central server providing access to large patient populations for research and quality improvement purposes across the healthcare system [78]. Federated learning is gaining traction in Europe, where data privacy regulations have a more risk-averse approach to AI development, thus favouring decentralized models [79]. In contrast, centralized learning approaches like Epic Cosmos are more common in the United States, where there is a more risk-tolerant environment which favours large-scale data aggregation.
In parallel, the use of synthetic data is emerging as a complementary strategy to enable data sharing while preserving patient privacy. Synthetic datasets are artificially generated to reflect the characteristics of real patient data and can be used to train and test models without exposing sensitive information [80]. The availability of large-scale data, may also support the creation of digital twins. Digital twins, or virtual simulations that mirror an individual’s biological and clinical state and rely on high-volume, high-fidelity datasets, may allow for predictive modelling and virtual testing of interventions before bedside application and improve safety of interventions.
The ICU community should advocate for the diffusion of further initiatives to extended collaborative AI models at national and international level.
Governance and regulation for AI in Critical Care
Despite growing regulatory efforts, AI regulation remains one of the greatest hurdles to clinical implementation, particularly in high-stakes environments like critical care, as regulatory governance, surveillance, and evaluation of model performance are not only conceptually difficult, but also require a large operational effort across diverse healthcare settings. The recent European Union AI Act introduced a risk-based regulatory framework, classifying medical AI as high-risk and requiring stringent compliance with transparency, human oversight, and post-market monitoring [18]. While these regulatory efforts provide foundational guidance, critical care AI presents unique challenges requiring specialized oversight.
By integrating regulatory, professional, and institutional oversight, AI governance in critical care can move beyond theoretical discussions toward actionable policies that balance technological innovation with patient safety [73, 81, 82].
Grant collaboration between public and private sector
Given the complexity and significant economic, human, and computational resources needed to develop a large generative AI model, physicians and regulators should promote partnerships among healthcare institutions, technology companies, and governmental bodies to support the research, development, and deployment of AI-enabled care solutions [83]. Beyond regulatory agencies, professional societies and institutional governance structures must assume a more active role. Organizations such as Society of Critical Care Medicine (SCCM), European Society of Intensive Care Medicine (ESICM), and regulatory bodies like the European Medical Agency (EMA) should establish specific clinical practice guidelines for AI in critical care, including standards for model validation, clinician–AI collaboration, and accountability. Regulatory bodies should operate at both national and supranational levels, with transparent governance involving multidisciplinary representation—including clinicians, data scientists, ethicists, and patient advocates—to ensure decisions are both evidence-based and ethically grounded. To avoid postponing innovation indefinitely, regulation should be adaptive and proportionate, focusing on risk-based oversight and continuous post-deployment monitoring rather than rigid pre-market restrictions. Furthermore, implementing mandatory reporting requirements for AI performance and creating hospital-based AI safety committees could offer a structured, practical framework to safeguard the ongoing reliability and safety of clinical AI applications.
Address AI divide to improve health equality
The adoption of AI may vary significantly across various geographic regions, influenced by technological capacities, (i.e. disparities in access to software or hardware resources), and differences in investments and priorities between countries. This “AI divide” can separate those with high access to AI from those with limited or no access, exacerbating social and economic inequalities.
The EU commission has been proposed to act as an umbrella to coordinate EU wide strategies to reduce the AI divide between European countries, implementing coordination and supporting programmes of activities [84]. The use of specific programmes, such as Marie-Curie training networks, is mentioned here to strengthen the human capital on AI while developing infrastructures and implementing common guidelines and approaches across countries.
A recent document from the United Nations also addresses the digital divide across different economic sectors, recommending education, international cooperation, and technological development for an equitable AI resource and infrastructure allocation [85].
Accordingly, the medical community in each country should lobby at both national level and international level through society and WHO for international collaborations, such as through the development of specific grants and research initiatives. Intensivist should require supranational approaches to standardized data collection and require policies for AI technology and data analysis. Governments, UN, WHO, and scientific society should be the target of this coordinated effort.
Continuous evaluation of dynamic models and post-marketing surveillance
A major limitation in current regulation is the lack of established pathways for dynamic AI models. AI systems in critical care are inherently dynamic, evolving as they incorporate new real-world data, while most FDA approvals rely on static evaluation. In contrast, the EU AI Act emphasizes continuous risk assessment [18]. This approach should be expanded globally to enable real-time auditing, validation, and governance of AI-driven decision support tools in intensive care units, as well as applying to post-market surveillance. The EU AI Act mandates ongoing surveillance of high-risk AI systems, a principle that we advocate to be adopted internationally to mitigate the risks of AI degradation and bias drift in ICU environments. In practice, this requires AI commercial entities to provide post-marketing surveillance plans and to report serious incidents within a predefined time window (15 days or less) [18]. Companies should also maintain this monitoring as the AI systems evolve over time. The implementation of these surveillance systems should include standardized monitoring protocols, embedded incident reporting tools within clinical workflows, participation in performance registries, and regular audits. These mechanisms are overseen by national Market Surveillance Authorities (MSAs), supported by EU-wide guidance and upcoming templates to ensure consistent and enforceable oversight of clinical AI systems.
Require adequate regulations for AI deployment in clinical practice
Deploying AI within complex clinical environments like the ICU, acute wards, or even regular wards presents a complex challenge [86].
We underline three aspects for adequate regulation: first, a rigorous regulatory process for evaluation of safety and efficacy before clinical application of AI products. A second aspect is related with continuous post-market evaluation, which should be mandatory and conducted according to other types of medical devices [18].
The third important aspect is liability, identifying who should be held accountable if an AI decision or a human decision based on AI leads to harm. This relates with the necessity for adequate insurance policies. We urge regulatory bodies in each country to provide regulations on these issues, which are fundamental for AI diffusion.
We also recommend that both patients and clinicians request that regulatory bodies in each country update current legislation and regulatory pathways, including clear rules for insurance policies to anticipate and reduce the risk for case laws.
| 2025-12-14T00:00:00 |
2025/12/14
|
https://ccforum.biomedcentral.com/articles/10.1186/s13054-025-05532-2
|
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Post Graduate Diploma AI in Healthcare (in collaboration with Max)
|
Post Graduate Diploma AI in Healthcare (in collaboration with Max)
|
https://www.bennett.edu.in
|
[] |
Co-designed with Max Healthcare doctors and AI experts. Covers AI-based medical imaging, predictive diagnostics, health data analytics, and more. Hospital ...
|
Head, Department of Biotechnology
Dean, School of Engineering & Applied Sciences
It gives me great pleasure to introduce the PG Diploma program in AI for Healthcare to aspiring participants. Recognizing the urgent need to define Artificial Intelligence (AI) ’s place in health & medicine, Bennett University has partnered with Max Healthcare, in truly preparing India to move with the Times.
As a researcher in Bioinformatics & Computational Biology for over 20 years, I am aware of the role of computation in understanding disease biology and addressing unmet medical challenges. With the ever-increasing volume of data generated from advancement in technologies, integration of AI in healthcare holds promising potential in disease diagnosis, therapeutics & patient care.
Bennett University in partnership with Max Healthcare aims to equip professionals with theoretical knowledge and tools to understand & apply Artificial Intelligence in clinical settings. By bringing together faculty experts in Artificial Intelligence, Computational Biology and Medical Sciences, we are uniquely positioned to highlight the transformative potential of AI. This course provides a valuable platform for participants from diverse backgrounds to explore and excel at the intersection of Artificial Intelligence and healthcare.
As Dean, School of Engineering & Applied Sciences I encourage you to explore this program and thoughtfully consider advancing your career in AI applications for healthcare.
| 2022-12-01T00:00:00 |
https://www.bennett.edu.in/progrmas/post-graduate-diploma-ai-healthcare/
|
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|
AI in EHR: Complete Guide to Seamless Integration & Use Cases
|
AI in EHR: Complete Guide to Seamless Integration & Use Cases
|
https://appinventiv.com
|
[
"Chirag Bhardwaj",
"Vp - Technology",
"Chirag Bhardwaj Is A Technology Specialist With Over Years Of Expertise In Transformative Fields Like Ai",
"Ml",
"Blockchain",
"Ar Vr",
"The Metaverse. His Deep Knowledge In Crafting Scalable Enterprise-Grade Solutions Has Positioned Him As A Pivotal Leader At Appinventiv",
"Where He Directly Drives Innovation Across These Key Verticals."
] |
Discover how AI in EHR can streamline healthcare operations & improve patient outcomes. Explore use cases, step-by-step integration, ...
|
copied!
Key takeaways: Not adopting AI in EHR/EMR today risks falling behind.
AI streamlines EHR workflows, cutting clinical and admin delays.
Real-world hospitals are already seeing faster, smarter care with AI.
Seamless AI integration needs system checks, data prep, and training.
AI in EHR tackles real challenges like bias, cost, and compliance.
Custom AI integration costs vary by use case, size, and integration depth.
Future trends show AI will fully automate, personalize, and connect EHR systems.
Every healthcare leader knows the frustration: piles of paperwork, delayed records, and patient care slipping through the cracks due to manual inefficiencies. Despite all the advances in medical technology, Electronic Health Records (EHR) and Electronic Medical Records (EMR) systems often feel more like obstacles than solutions. These systems, intended to streamline operations, still leave teams bogged down in administrative tasks, creating more work instead of alleviating it.
This is where Artificial Intelligence (AI) steps in—no longer a distant possibility, but a current reality. As we move through 2025, the integration of AI into EHR/EMR systems, will push boundaries and achieve what was once unimaginable. It’s beyond automating tasks; it’s transforming the entire healthcare experience, offering real-time insights, predictive analytics, and dynamic data management that drives better decision-making and patient outcomes.
If you’re not leveraging AI in EHR/EMR systems today, you risk falling behind. We don’t think you want to struggle with outdated systems and drown in administrative overload while others are reaping the rewards of AI-driven efficiency. The stakes are too high, and the gap between those who adapt and those who don’t is widening by the day. The choice is clear: either embrace AI to revolutionize your EHR/EMR systems or risk falling behind in a rapidly advancing healthcare landscape.
In this blog, we’ll guide you through how AI is revolutionizing EHR/EMR integration, offering concrete steps, benefits, and potential hurdles. Get ready to discover why AI is not just an option, but a must-have for the future of healthcare operations.
Don’t let legacy EHR/EMR systems hold you back. AI is transforming healthcare—get ahead or get left behind. Let’s help you lead the change.
The State of AI in Healthcare: Why 2025 Is the Year to Act
Electronic Health Records (EHR) and Electronic Medical Records (EMR) have long been the cornerstone of healthcare systems. These systems house vital patient data, facilitating clinical decision-making and improving overall care coordination. However, despite their critical role, many EHR and EMR systems still operate in silos, requiring manual data entry and offering limited real-time insights. This often leads to administrative burdens, delayed diagnoses, and potential gaps in patient care.
Now, AI is already becoming the norm, driving transformation across the board. Healthcare businesses who have already integrated AI have experienced significant improvements in the patient outcomes, the overall operational efficiency, and the cost savings. AI is shifting from being a supplementary technology to being a driving force in healthcare modernization.
Recent industry trends strongly highlight the rising importance of AI in healthcare, especially in transforming EHR and EMR systems, streamlining workflows, improving decision-making, and enhancing patient outcomes. McKinsey highlights that AI integration with healthcare systems could result in net savings of up to $360 billion annually by streamlining administrative processes and improving diagnostic accuracy. Deloitte also reports that 75% of top healthcare organizations are already exploring or preparing to scale Generative AI across their operations, all thanks to the vast potential of Generative AI to reduce inefficiencies and improve patient outcomes.
Additionally, PwC reveals that 77% of healthcare executives consider AI among their top three investment priorities followed by the Internet of Things (IoT), augmented reality (AR), and virtual reality (VR). Other important technologies like advanced robotics, blockchain, quantum computing, and neural interfaces were also part of their future plans.
The shift toward using AI to improve Electronic Health Records is not just about keeping up with technological advances; it’s about improving patient outcomes, enhancing operational efficiency, and staying ahead of the competition. As the healthcare sector embraces AI with EHR/EMR systems, the benefits of AI EHR will continue to grow, providing faster access to data, better clinical decisions, and reduced administrative costs.
Use Cases of AI in EHR/EMR: Automating Administrative Tasks to Providing Real-time Clinical Decision Support
The integration of AI into EHR/EMR systems has opened up a wide array of possibilities, transforming how healthcare providers manage and use patient data. As we explore the detailed use cases below, it’s clear that AI is no longer a futuristic concept but a key enabler of better patient care, streamlined operations, and cost reductions. From automating administrative tasks to providing real-time clinical decision support, AI EHR systems are revolutionizing healthcare operations.
1. Automating Data Entry and Documentation
One of the most time-consuming aspects of EHR systems is manual data entry. Healthcare professionals often spend a significant amount of time inputting patient data, making the process prone to errors and delays. AI in EHR systems can automate data entry by extracting information from patient records, clinical notes, and even voice inputs using natural language processing (NLP) in healthcare systems. This reduces human error, enhances efficiency, and allows healthcare providers to focus on patient care rather than administrative tasks.
Real-World Example: Nuance Communications has integrated AI-driven voice recognition and NLP technology with their EHR systems at several hospitals, including Cedars-Sinai Medical Center. The system automatically transcribes and organizes doctor-patient interactions, reducing the time spent on documentation by more than 50%. This has significantly improved workflow and efficiency, enabling clinicians to spend more time with patients.
Discover how we developed YouCOMM, an AI-powered healthcare system that enables the patients to get in touch with hospital staff through voice commands! View Case Study
2. Predictive Analytics for Patient Outcomes
AI can analyze vast amounts of patient data from EHR/EMR systems to predict future health risks, such as the likelihood of disease progression, readmission, or complications. By identifying at-risk patients early, healthcare providers can intervene before conditions worsen, improving patient outcomes and reducing healthcare costs. AI-powered predictive analytics can help hospitals optimize resource allocation and prioritize high-risk patients for timely intervention.
Real-World Example: Mount Sinai Health System in New York has implemented AI to predict patient readmissions within 30 days using data from their EHR systems. By leveraging AI algorithms, they can flag patients at high risk of readmission, allowing them to proactively address potential issues. This initiative has led to a 20% reduction in readmission rates, improving both patient outcomes and operational efficiency.
3. Clinical Decision Support
AI-powered clinical decision support tools embedded in EHR/EMR systems can assist healthcare providers in making evidence-based decisions. These tools analyze patient data, medical histories, and the latest research to offer recommendations on diagnosis, treatment options, and medication management. By providing clinicians with actionable insights, AI enhances the accuracy of clinical decisions, reduces diagnostic errors, and helps optimize patient care.
Real-World Example: Memorial Sloan Kettering Cancer Center used IBM Watson for Oncology to analyze cancer patients’ medical records and provide oncologists with personalized treatment recommendations based on vast amounts of medical literature. This integration helped doctors make more informed decisions, improving patient outcomes.
4. Real-Time Risk Alerts and Monitoring
AI in EMR systems can provide real-time alerts and monitoring for high-risk patients. By continuously analyzing data such as vital signs, lab results, and medication, AI can flag any abnormalities or changes in a patient’s condition that may require immediate attention. This proactive monitoring allows healthcare providers to intervene early and prevent adverse events, enhancing patient safety and reducing healthcare costs.
Real-World Example: Cerner has integrated AI-driven real-time monitoring into its EHR systems at The University of Kansas Health System. The system continuously analyzes patient data and alerts healthcare providers to any critical changes, such as sudden drops in blood pressure or oxygen levels. This has led to quicker interventions and a reduction in the incidence of preventable complications, such as sepsis.
5. AI-Powered Natural Language Processing (NLP) for Improved Patient Records
One of the challenges with EHR systems is that a large amount of clinical data is often unstructured, existing in the form of free text or voice notes. AI and NLP can extract valuable insights from these unstructured data sources, making it easier to categorize, analyze, and retrieve relevant information from patient records. This improves the quality of data in EHR/EMR systems, enabling better decision-making and more accurate documentation.
Real-World Example: Mayo Clinic has implemented AI and NLP technology to improve the accuracy of patient records in their EHR systems. Using NLP, the clinic can extract key information from clinical notes, enabling faster and more accurate diagnoses. This use of AI has not only improved the quality of their patient records but also enhanced their ability to deliver personalized care.
6. Personalized Treatment Plans Through AI
Artificial intelligence for EHR systems can help develop personalized treatment plans by analyzing a patient’s medical history, genetic information, lifestyle data, and the latest clinical research. By tailoring treatment plans to individual patients, AI can help optimize care and improve outcomes. This personalized approach also allows for better management of chronic conditions and the reduction of adverse reactions to medications.
Real-World Example: Tempus has integrated AI and machine learning with EHR systems to provide personalized cancer treatment recommendations. By analyzing a patient’s genetic profile and treatment history, the platform offers oncologists customized treatment options, leading to more effective and targeted therapies. Tempus’s AI-driven insights have helped clinicians deliver more precise and successful treatments.
7. AI for Population Health Management
AI can aggregate data from multiple EHR/EMR systems to identify trends and patterns in patient populations, such as the prevalence of certain conditions, risk factors, and social determinants of health. This enables healthcare organizations to develop strategies for improving public health, reducing health disparities, and managing population health more effectively. AI in population health management also allows for better resource allocation and more targeted healthcare interventions.
[Also Read: 10 Use Cases on How AI is Transforming Genomics]
Real-World Example: Geisinger Health System uses AI in its EHR system to monitor population health and identify at-risk groups. By analyzing data on patients’ medical history, socioeconomic status, and lifestyle factors, Geisinger is able to provide tailored care to at-risk populations, improving health outcomes and reducing unnecessary hospitalizations.
8. AI-Enhanced Medical Imaging Integration with EHR Systems
AI’s ability to analyze medical images, such as X-rays, MRIs, and CT scans, has greatly improved diagnostic accuracy. By integrating AI-powered image recognition with EHR/EMR systems, healthcare providers can quickly access and analyze medical images alongside patient data. This integration allows for faster, more accurate diagnoses and ensures that patients receive the right treatment without unnecessary delays.
Real-World Example: Radiology Partners, the largest physician-led radiology practice in the U.S., uses AI to assist in reading and interpreting medical images. The AI system is integrated with their EHR, enabling radiologists to quickly identify issues like tumors or fractures in medical images, leading to faster diagnosis and treatment. This integration has significantly reduced turnaround times for diagnostic results and improved patient care.
9. AI-Driven Patient Scheduling and Resource Allocation
AI-powered systems in EHR/EMR platforms are being used to optimize patient scheduling and resource allocation. By analyzing historical data and predicting demand, AI can suggest the best times for appointments, manage waitlists, and even adjust resources dynamically based on patient volume. This optimization reduces patient wait times, prevents overbooking, and maximizes the use of healthcare facilities.
Real-World Example: Mayo Clinic is also using AI to improve electronic health records. The organization has adopted AI-driven scheduling tools in their EHR system to optimize patient flow. By analyzing patient history, availability, and clinician schedules, the AI suggests the best appointment times, ensuring that resources are used efficiently. This has decreased appointment cancellations by 15% and improved patient satisfaction by streamlining scheduling processes.
10. Enhancing Clinical Trials Recruitment
AI can be used to streamline the recruitment process for clinical trials by analyzing EHR/EMR data to match patients with relevant trials. By quickly identifying eligible candidates based on their medical histories, conditions, and other criteria, AI can accelerate the recruitment process and ensure that trials are better aligned with patient needs.
Real-World Example: Pfizer uses AI to analyze patient data within EHR/EMR systems for clinical trial recruitment. By cross-referencing patient records with trial criteria, Pfizer has been able to match eligible participants faster, improving the efficiency of its clinical trials and reducing recruitment time by up to 25%.
11. AI-Powered Chatbots for Patient Engagement
AI chatbots integrated with EHR/EMR systems are increasingly being used to improve patient engagement. These bots can answer questions, provide appointment reminders, and even collect patient information prior to visits. They also assist in triaging symptoms and offering health advice, allowing healthcare providers to focus on more complex patient concerns.
Real-World Example: Babylon Health employs AI-powered chatbots integrated with its EHR systems to provide virtual consultations and health information. The chatbot guides patients through symptom checkers, schedules appointments, and directs them to the appropriate healthcare professionals, enhancing both patient engagement and the efficiency of the healthcare process.
12. Drug Interaction and Prescription Monitoring
AI EHR systems can analyze patient records to identify potential drug interactions and flag medication errors in real-time. By cross-referencing prescriptions with the patient’s medical history, AI can alert healthcare providers to harmful interactions, allergies, or contraindications, reducing medication errors and improving patient safety.
Real-World Example: Epic Systems has integrated AI-driven prescription monitoring in their EHR system. At Children’s Hospital Colorado, the system detects potential drug interactions by scanning the entire patient’s prescription history, lab results, and allergies. This has significantly reduced the incidence of adverse drug reactions and improved medication safety for pediatric patients.
13. Seamless Billing and Coding Automation
AI can automate the process of medical billing and coding, which is a time-consuming task that often involves manual verification and error correction. AI-powered systems can automatically assign the correct codes to medical procedures and diagnoses by analyzing patient data in the EHR system. This increases the accuracy of billing, accelerates reimbursement, and reduces administrative costs.
Real-World Example: Optum360 has integrated AI with EHR systems to streamline the coding and billing process for healthcare providers. By using AI to automate the assignment of ICD-10 codes and procedural codes, the company has reduced errors in billing by 20% and improved claims processing time, leading to faster reimbursements.
14. AI-Powered Disease Surveillance
AI can help monitor and track disease outbreaks by analyzing patient data from EHR/EMR systems. By detecting patterns of symptoms, AI can identify emerging diseases or outbreaks in real-time, providing healthcare providers with the information needed to respond quickly and allocate resources accordingly. This proactive approach can significantly enhance public health management.
Real-World Example: Kaiser Permanente, one of the largest integrated health systems in the United States, leverages its vast electronic medical-record database and in-house machine-learning models to spot unusual spikes in flu-like or respiratory symptoms across its member population. When the AI engine flags a clustering pattern at certain clinics, the public-health team receives an alert within hours, enabling them to speed up testing, shift vaccine supplies, and coordinate outreach in the affected neighborhoodsneighbourhoods—often days before standard reporting channels would raise the same alarm.
15. AI for Remote Patient Monitoring Integration
With the growing popularity of telehealth, integrating Artificial intelligence intofor EHR systems allows for real-time monitoring of patients’ vital signs, especially for those with chronic conditions. AI can analyze data from wearables and other remote monitoring devices, alerting healthcare providers to significant changes that may require immediate attention.
Real-World Example: Philips Healthcare uses AI to analyze data from remote patient monitoring devices integrated with EHR/EMR systems. By monitoring chronic disease patients in real-time, Philips has helped healthcare providers detect early signs of exacerbation, enabling them to intervene before patients require hospitalization. This has improved patient outcomes and reduced hospital admissions for chronic conditions.
16. Fraud Detection and Prevention
AI in EHR/EMR systems can help detect fraudulent activity by analyzing patterns in billing and patient records. Machine learning algorithms can identify inconsistencies, such as duplicate claims or suspicious billing practices, and flag them for review. This ensures that healthcare organizations maintain compliance and protect against financial fraud.
[Also Read: 10 Use Cases and Benefits of AI in Medical Billing]
Real-World Example: Optum uses AI-powered fraud detection tools within its EHR system to identify suspicious billing patterns. By analyzing billing codes, patient data, and healthcare provider activities, the system flags potential fraud, allowing the company to take immediate action. This has reduced fraudulent claims and improved financial security for healthcare providers.
17. Streamlined Discharge Planning
AI in EHR/EMR systems can be used to improve discharge planning by analyzing patient data and predicting the level of care needed after hospital discharge. AI can suggest discharge instructions, follow-up appointments, and home care needs based on the patient’s condition, ensuring that patients receive appropriate care after they leave the hospital.
Real-World Example: Cigna Health has implemented AI in their EHR system to optimize discharge planning. By analyzing data from the patient’s hospital stay, the system recommends follow-up care and at-home support, ensuring that patients receive the necessary care after discharge. This approach has helped reduce readmission rates and improved overall patient satisfaction.
The Step‑By‑Step AI in EHR/EMR Integration Guide
Integrating AI into EHR/EMR systems is a transformative journey that can enhance healthcare delivery, streamline operations, and improve patient outcomes. Below is a detailed step-by-step guide to successfully integrate AI with your EHR/EMR system.
1. Conduct a Comprehensive EHR/EMR System Assessment
Before integrating AI, it is essential to assess your current EHR/EMR system to understand its strengths and limitations. This step helps to identify pain points that AI can address, such as automating data entry, enhancing clinical decision support, or enabling predictive analytics.
What It Does:
Evaluates the current capabilities of your EHR/EMR system
Identifies areas for improvement and how AI can add value
Helps ensure compatibility with AI solutions
Addresses data privacy and compliance requirements
2. Define AI Use Cases for EHR/EMR Integration
Once the assessment is complete, the next step is to define the AI use cases that align with your organization’s goals. Use cases may include automating administrative tasks, predictive analytics for patient outcomes, or providing clinical decision support.
What It Does:
Clarifies the specific AI-driven goals for the organization
Ensures alignment of AI tools with the healthcare provider’s needs
Helps prioritize AI initiatives based on impact
Determines the resources required for each use case
3. Choose the Right AI Tools and Technology Stack
This step involves selecting the right AI tools and technology stack that are compatible with your EHR/EMR systems. It is important to choose AI solutions with a proven track record in healthcare and that meet your technical and regulatory requirements.
What It Does:
Ensures compatibility between AI tools and existing EHR/EMR systems
Identifies trusted vendors with experience in healthcare AI
Aligns AI solutions with your technological infrastructure
Guarantees compliance with privacy and regulatory standards
4. Data Preparation and Standardization
AI models require clean, structured, and standardized data to be effective. This step focuses on preparing the data by cleaning it, ensuring consistency, and converting unstructured data into a usable format. Standardizing data ensures interoperability with other systems and AI tools.
What It Does:
Ensures that data is accurate, complete, and properly formatted
Standardizes data to meet healthcare data exchange standards (e.g., FHIR, HL7)
Prepares unstructured data for AI analysis
Addresses missing data through data augmentation or enrichment
5. Implement AI Models and Integrate with EHR/EMR Systems
With the data prepared, AI models are implemented and integrated into the EHR/EMR system. This process ensures that AI tools, such as predictive analytics or decision support, are embedded within the existing system, allowing them to interact with patient data and provide insights in real-time.
What It Does:
Integrates AI models directly into clinical workflows
Embeds predictive and decision support tools into EHR/EMR systems
Enables AI-driven analysis of patient data for actionable insights
Ensures seamless interaction between AI tools and EHR/EMR user interfaces
6. Train Healthcare Staff on AI Usage
Training healthcare professionals on how to use AI-powered tools is critical to ensure that the integration is successful. This step focuses on educating staff about how to interpret AI-driven recommendations and incorporate them into their daily workflows.
What It Does:
Provides healthcare providers with the knowledge to effectively use AI tools
Helps clinicians understand AI recommendations and insights
Improves adoption rates by familiarizing staff with new technologies
Enhances the confidence of clinicians in using AI for clinical decision-making
7. Monitor and Optimize AI Performance
AI systems require continuous monitoring to ensure that they perform effectively and provide accurate recommendations. This step focuses on tracking the performance of AI tools, identifying areas for improvement, and optimizing models for better results.
What It Does:
Tracks the performance and accuracy of AI-driven outputs
Identifies and addresses issues such as errors or biases in AI predictions
Continuously updates AI models to incorporate new data and research
Optimizes AI tools to align with evolving clinical needs and goals
8. Ensure Compliance with Regulations and Standards
AI systems in healthcare must adhere to various regulations, including HIPAA in the U.S. This step ensures that AI integration complies with all relevant data privacy and security standards.
What It Does:
Ensures that AI models comply with privacy regulations like HIPAA and GDPR
Implements encryption protocols to secure patient data processed by AI
Regularly audits AI systems for compliance with healthcare regulations
Maintains transparency and accountability in AI usage for patient data
9. Scale the AI Integration Across the Organization
Once AI integration has been successfully implemented in one department or use case, the next step is to scale the technology across the entire organization. This includes expanding AI tools to other specialties, departments, or patient populations.
What It Does:
Expands AI usage across different clinical specialties or care settings
Ensures that AI-driven tools are accessible organization-wide
Provides support for scaling AI tools to additional departments
Ensures consistency in AI-driven processes across the organization
10. Regularly Update and Innovate
AI technology is constantly evolving, and it’s crucial to keep your AI models and tools up to date. This step involves staying informed about the latest AI advancements, regularly updating AI models, and fostering a culture of innovation.
What It Does:
Ensures that AI tools are up to date with the latest technology advancements
Incorporates new data, research, and clinical guidelines into AI models
Encourages continuous improvement and innovation in AI solutions
Ensures long-term relevance and effectiveness of AI tools in healthcare operations
Ensuring HIPAA Compliance and Data Security in AI Integration
When integrating AI into EHR/EMR systems, ensuring HIPAA compliance and protecting Protected Health Information (PHI) is non-negotiable. AI solutions process vast amounts of sensitive patient data—any misstep can lead to legal issues, reputational damage, and patient distrust. Here’s how to secure your AI-powered healthcare infrastructure while meeting all U.S. regulatory standards:
Technical Safeguards for PHI Protection
Healthcare organizations must implement robust technical controls that align with HIPAA’s Security Rule.
Access Controls : Restrict data access based on roles. Implement multifactor authentication (MFA) and session timeouts for all users interacting with EHR-AI systems.
Data Encryption : Encrypt PHI both at rest and in transit using AES-256 or TLS 1.2+ protocols to prevent unauthorized interception or breaches.
Audit Logs : Maintain detailed logs of all system activity, including who accessed patient data, when, and for what purpose. These logs are essential for compliance audits and breach forensics.
PHI De-identification for AI Training
AI models often need real patient data for training, which introduces compliance risks. To mitigate this:
De-identify PHI following HIPAA’s Safe Harbor method—remove all 18 identifiers (e.g., names, dates, medical record numbers).
Use synthetic data where feasible to train AI models without exposing actual PHI.
Maintain data lineage to ensure de-identified data cannot be reverse-engineered.
Business Associate Agreements (BAAs)
Every third-party tech partner involved in storing, processing, or transmitting PHI must sign a Business Associate Agreement.
Ensure your AI and cloud vendors understand and adhere to HIPAA regulations.
Your BAA should define responsibilities around breach notification, security practices, and termination procedures.
Appinventiv, as your technology partner, signs BAAs and adheres to strict HIPAA protocols during the entire AI implementation lifecycle.
Interoperability Standards: HL7 & FHIR
AI integration must support seamless data exchange with other health IT systems. That’s only possible through adherence to interoperability standards like:
HL7 (Health Level Seven): Facilitates structured data exchange between EHRs, labs, and imaging systems.
Facilitates structured data exchange between EHRs, labs, and imaging systems. FHIR (Fast Healthcare Interoperability Resources): A modern API-driven standard that enables real-time data access and exchange across devices and platforms.
Supporting HL7 and FHIR ensures your AI tools work across your entire digital health ecosystem, from diagnostics to discharge.
Understanding the Cost of Integrating AI in EHR/EMR Systems
The cost of integrating AI in EHR/EMR systems typically ranges from $50,000 to $500,000 or more, depending on various factors. The cost variation depends on the size of the organization, the complexity of the AI use cases in EHR, and the customization required for integration. Here’s a quick look at the key factors affecting cost of AI EHR/EMR software:
1. Size and Scale of the Organization
Larger organizations require more extensive AI integration, leading to higher costs due to more complex systems and wider deployment needs.
2. Complexity of AI Use Cases
Advanced AI-powered solutions in EHR, like predictive analytics or clinical decision support, typically cost more than simple tasks such as automating data entry.
3. Customization and Integration Needs
Customizing AI tools for EMR systems to meet specific organizational requirements and integrating them with existing workflows can drive up costs.
4. Data Preparation and Standardization
Preparing and structuring data for AI models in EHR/EMR systems requires additional resources, especially for unstructured data, increasing the overall cost.
5. AI Tool Selection and Vendor Costs
The choice between pre-built solutions and custom-built AI models for EMR/AI integration will affect pricing, with premium vendors often coming at a higher cost.
6. Training and Change Management
Training staff on using AI-driven EHR systems and managing the transition can add to the cost, ensuring smooth adoption and reducing resistance.
7. Ongoing Maintenance and Updates
AI models require continuous updates and maintenance, particularly as new data flows into the EHR systems, leading to additional costs over time.
8. Compliance and Security
Meeting HIPAA compliance and ensuring data security are essential for AI EMR systems, adding extra expenses for encryption and audits.
Curious about what AI in EHR/EMR would cost for your specific setup? We can provide precise, tailored estimates—no guesswork. Get in touch
Challenges and Solutions in AI Integration with EHR/EMR Systems
Integrating AI into EHR/EMR systems brings tremendous benefits, but it also presents a series of challenges that need to be addressed for a successful implementation. Below is a detailed breakdown of common challenges and limitations of AI EMR and EHR software solutions:
Challenge What It Means How Businesses Can Solve It Data Quality and Standardization Issues The success of AI in EHR systems depends on the quality and structure of the data it processes. Unstructured, fragmented, or incomplete data can hinder the effectiveness of AI models in EMR systems. Businesses should invest in robust data cleaning, standardization, and normalization strategies. Utilizing Natural Language Processing (NLP) can help convert unstructured data into structured formats, ensuring compatibility with AI EMR tools. Adhering to standards like FHIR and HL7 ensures seamless integration with other healthcare systems and improved AI output. Data Privacy and Compliance Concerns The use of AI with EHR/EMR systems requires handling sensitive patient data, raising concerns about compliance with privacy regulations such as HIPAA and GDPR. Mishandling can lead to legal issues. Businesses must implement strict data encryption protocols, access control systems, and privacy measures to comply with HIPAA and GDPR. Integrating AI in EMR systems that adhere to these regulations and conducting regular audits ensures that patient data is handled securely and within legal boundaries. Integration Complexity with Legacy Systems Many healthcare organizations still rely on outdated EHR/EMR systems that may not be compatible with modern AI-driven technologies. This makes AI integration with EHR systems challenging and complex. Healthcare businesses should choose AI in EHR solutions with flexible, modular architectures and robust API-based integration options. In some cases, upgrading legacy systems to support AI EMR tools may be necessary, or businesses can consider cloud-based AI solutions to bypass legacy system limitations. Clinical Adoption and Trust in AI Clinicians may be skeptical about the accuracy and reliability of AI-powered clinical decision support tools within EHR systems, leading to hesitation in adopting AI tools for clinical decisions with AI in EHR systems. To foster trust, businesses should involve healthcare providers early in the process, providing evidence of AI use cases in EHR through pilot projects. Offering extensive training and demonstrating AI-driven clinical decision support’s ability to enhance clinical decision-making rather than replace human expertise helps increase trust and adoption. Bias in AI Models and Data AI in EMR systems can inherit biases from the data they are trained on, leading to skewed results and potentially affecting underrepresented patient populations. Businesses should ensure their AI in EHR models are trained on diverse, representative datasets that reflect the demographics of the patient population. Regular audits should be conducted to identify and address biases (training data can perpetuate racial or socioeconomic biases), and ensure responsibility. AI models in EHR systems should be adjusted to improve fairness in decision-making. Cost and Resource Constraints The financial and resource requirements to integrate AI in EHR systems can be significant, especially for smaller healthcare organizations with limited budgets. Businesses can adopt a phased approach, starting with high-impact areas such as AI-powered clinical decision support or predictive analytics for patient outcomes. Leveraging cloud-based AI in EMR systems reduces infrastructure costs, while choosing AI vendors with affordable pricing models and subscription-based services can spread out expenses. AI Model Transparency and Explainability AI in EHR systems may sometimes function as a “black box,” with recommendations that are not fully understood or explained, leading to concerns over the reliability of AI-driven clinical decisions. To improve transparency, businesses should focus on developing explainable AI models that offer clear, understandable reasons behind the AI-driven recommendations in clinical decisions with AI in EHR systems. Implementing explainable AI (XAI) systems helps clinicians better understand how AI models arrive at their decisions, thereby improving trust in the system. Interoperability with External Systems Healthcare organizations often use multiple systems (e.g., lab management, imaging), and seamless data flow between these systems is essential for comprehensive patient care. Ensure AI EMR systems support healthcare interoperability through standards like HL7. Implementing AI with EHR/EMR systems that are designed to communicate effectively with other platforms ensures a seamless exchange of patient data, improving care coordination and reducing inefficiencies in healthcare delivery. Managing the Change and Resistance Integrating AI into clinical workflows can lead to resistance from staff due to fears of disrupting established procedures or replacing human jobs. Overcome resistance by offering clear communication about how AI in EMR tools will enhance, not replace, clinical workflows. Provide evidence of AI’s ability to reduce administrative burden and improve patient outcomes. Involve clinicians in the AI adoption process, offering training and feedback loops to foster a sense of ownership and acceptance. Scalability of AI Solutions Scaling AI integration across a large healthcare system or multiple departments can be complex, requiring significant coordination and resource allocation. Start with scalable, modular tools for AI and EHR that can be implemented in high-priority areas like predictive analytics or documentation automation. Once AI models prove effective in one area, they can be expanded to other departments. Cloud-based solutions can also help businesses scale their AI implementation efficiently without the need for heavy infrastructure investments.
Tackling AI integration hurdles? From system complexities to compliance barriers—we’ve solved them all. Let’s build your AI-driven future, challenge-free. View Our Services
Addressing Bias in AI Models and Patient Data: A Must-Have for Responsible EHR Integration
Bias in AI is more than a technical flaw—it’s a direct threat to equitable patient care and clinical credibility. When it comes to AI-powered EHR/EMR systems, biased models can lead to misdiagnosis, delayed treatments, and exclusion of underserved communities. For healthcare leaders, addressing bias is not just an ethical imperative—it’s a strategic necessity.
Where Does Bias in AI Come From?
Bias in EHR-integrated AI typically originates from three key areas:
Historical Data Skews : If your training data mostly includes patients from a specific demographic (e.g., urban, white, male), your AI model will generalize care decisions toward that group—neglecting others.
Labeling and Annotation Errors : Clinician-generated notes and diagnosis labels can include subjective opinions, which the AI may interpret as clinical truth.
Structural and Societal Biases : Broader healthcare disparities—like unequal access to care—can be encoded in datasets, reinforcing inequalities when AI models learn from them.
Real-World Risk: What Bias Looks Like in Practice
An AI model trained primarily on male cardiovascular data might underdiagnose heart disease in women. Similarly, a risk-scoring algorithm trained on cost-of-care data might rank low-income patients as lower-risk simply because they historically received less care—not because they were actually healthier.
[Also Read: Preventing AI Model Collapse: Addressing the Inherent Risk of Synthetic Datasets]
How to Build Fair and Unbiased AI for EHR Systems
To ensure fairness in AI-powered EHR/EMR systems, organizations must follow a proactive and repeatable framework:
1. Data Diversity and Representation
Collect and use training datasets that reflect demographic diversity, including ethnicity, age, gender, socioeconomic background, and geography.
Partner with data providers that offer curated, demographically inclusive datasets tailored for healthcare AI.
2. Bias Auditing and Fairness Testing
Regularly audit your AI models for disparate impact—measure accuracy and outcomes across subgroups.
Use fairness metrics like equalized odds, demographic parity, and false negative/positive rates to uncover hidden discrepancies.
3. Bias Mitigation Techniques
Apply re-sampling or re-weighting during training to balance data representation.
Use adversarial debiasing algorithms to minimize sensitive attribute influence (e.g., race or gender) on predictions.
Apply counterfactual testing—checking how a prediction would change if only a protected attribute (like race) were altered.
4. Transparent and Explainable AI (XAI)
Integrate explainable AI tools like SHAP or LIME to show how predictions were made and ensure clinicians can interpret model outputs with confidence.
Transparency boosts clinical trust and also helps reveal hidden biases in decision pathways.
5. Continuous Monitoring in Real-World Settings
Post-deployment, monitor outcomes continuously to detect real-world bias emergence.
Set up feedback loops where clinicians can flag suspicious outputs, which can inform retraining or model updates.
Future Trends of AI in Electronic Health Records and Electronic Medical Records
The integration of AI in Electronic Health Records (EHR) and Electronic Medical Records (EMR) is just the beginning. As AI technology continues to evolve, its role in healthcare will expand, transforming EHR/EMR systems into even more powerful, proactive tools. Here are some key future trends shaping AI in EHR/EMR systems.
1. Personalized Predictive Care Will Become the New Standard
Future AI EHR systems will go beyond basic predictions to offer hyper-personalized care journeys. By continuously learning from patient data, AI and EHR platforms will provide highly specific risk alerts and treatment plans tailored to individual needs.
2. Real-Time, AI-Driven Healthcare Ecosystems
The future of AI in EHR will focus on creating real-time, fully connected healthcare environments. AI with EHR/EMR systems will enable seamless data exchange across hospitals, pharmacies, labs, and remote care devices, improving both speed and accuracy in clinical workflows.
3. Smarter Voice-Enabled Clinical Workflows
Voice-powered documentation will become a core feature of Artificial Intelligence for EHR, reducing manual data entry. By integrating advanced voice recognition into AI EHR platforms, clinicians can capture patient notes quickly and accurately, minimizing errors and enhancing productivity.
4. Proactive AI Agents Will Support Clinicians
Future EMR AI systems will include intelligent healthcare agents that anticipate clinician needs. These assistants will proactively provide clinical suggestions, flag missing data, and automate repetitive tasks, further demonstrating the benefits of AI EHR in reducing cognitive load.
5. Seamless Multi-Department AI Coordination
Next-generation AI EMR systems will support real-time collaboration across departments like radiology, pathology, and cardiology. AI and EHR platforms will centralize insights, making it easier for specialists to access critical patient data instantly, improving interdisciplinary care.
6. Predictive Alerts Will Drive Preventative Care
The AI EHR of the future will offer instant, predictive alerts that help clinicians prevent complications before they arise. These proactive alerts will solidify the benefits of AI EHR in minimizing medical errors and improving patient safety.
7. Continuous Remote Care Through AI-Driven EMRs
EMR AI systems will strengthen remote patient care by continuously integrating real-time data from wearables and home monitoring devices. This will make AI in EMR a crucial tool in chronic disease management and post-discharge follow-up.
8. Smart Hospital Infrastructure Powered by AI and EHR
The future role of AI in Electronic Health Records will extend into smart hospitals, where AI in EHR systems will sync with IoT devices to manage beds, track equipment, and streamline emergency care, optimizing resource allocation and patient flow.
Why Appinventiv is the Right Partner to Develop AI in EHR/EMR Powered Solutions
At Appinventiv, we understand the complexities and opportunities that come with integrating AI in EHR/EMR systems. With our deep expertise in AI-powered healthcare solutions, we are uniquely positioned to help organizations navigate the complexities of AI adoption in their EHR systems.
Our team brings a wealth of experience working with top-tier healthcare providers, helping them transform patient care and streamline operations using cutting-edge AI technology. From predictive analytics and clinical decision support to seamless AI-powered documentation, we are ready to build AI-driven EMR solutions that meet your organization’s needs and exceed your expectations.
As a dedicated AI development services firm, here’s why we are the best choice for developing AI-powered EHR/EMR software:
Expertise in AI Integration: With a proven track record in AI development, we specialize in integrating AI in EHR/EMR systems, enhancing clinical decision-making, and automating workflows.
With a proven track record in AI development, we specialize in integrating AI in EHR/EMR systems, enhancing clinical decision-making, and automating workflows. Healthcare Industry Knowledge: As a custom healthcare app development firm, we have extensive experience working with healthcare providers, understanding the unique challenges and regulatory requirements involved in AI adoption.
As a custom healthcare app development firm, we have extensive experience working with healthcare providers, understanding the unique challenges and regulatory requirements involved in AI adoption. Customized Solutions: We provide tailored AI-powered solutions for EHR systems, ensuring they align with your organization’s specific needs and improve overall operational efficiency.
We provide tailored AI-powered solutions for EHR systems, ensuring they align with your organization’s specific needs and improve overall operational efficiency. End-to-End Support: From strategy and design to implementation and ongoing support, we offer full-service assistance, ensuring a smooth AI integration process.
From strategy and design to implementation and ongoing support, we offer full-service assistance, ensuring a smooth AI integration process. Compliance and Security Focus: We prioritize HIPAA and GDPR compliance, ensuring that all AI-driven solutions adhere to the highest standards of data security and patient privacy.
We prioritize HIPAA and GDPR compliance, ensuring that all AI-driven solutions adhere to the highest standards of data security and patient privacy. Scalability: Our solutions are designed to scale, allowing healthcare organizations to expand AI integration across departments and grow without compromising performance.
Our solutions are designed to scale, allowing healthcare organizations to expand AI integration across departments and grow without compromising performance. Proven Results: Our clients have experienced tangible improvements in efficiency, reduced administrative burdens, and enhanced clinical outcomes after implementing AI in EMR systems with our solutions.
Get in touch with us to unlock the full potential of AI in your EHR/EMR systems and lead the future of healthcare innovation.
FAQs
Q. How to integrate AI into EHR?
A. Integrating AI in EHR systems involves several stages, beginning with a thorough assessment of your current system. The first step is to understand what challenges or inefficiencies exist in the existing EHR/EMR system that AI can address, such as automating administrative tasks or enhancing clinical decision support. Once this is clear, it’s essential to select the right AI-powered tools that can integrate seamlessly into your EHR systems.
Assess your current system: Understand the strengths and weaknesses of your existing EHR system.
Define the AI use cases: Choose specific applications such as predictive analytics or real-time decision support.
Select the right AI tools: Choose AI solutions for EMR systems that align with your current infrastructure and comply with regulations like HIPAA.
Q. How much does a custom AI EHR cost?
A. The cost of custom AI EHR systems can range between $50,000 to $500,000 or more, depending on the specific needs and scale of the organization. The variation in cost arises due to the level of complexity of the AI models, the integration requirements, and the healthcare organization’s size. Larger systems and more advanced tools, such as AI-powered clinical
Q. How does AI integration with EHRs improve patient care and operational efficiency?
A. AI integration with EHR systems significantly enhances both patient care and operational efficiency. AI-driven tools can streamline administrative tasks such as data entry and billing, which allows healthcare providers to focus more on direct patient care. Additionally, AI in EHR systems can provide real-time clinical decision support, improving diagnostic accuracy and enabling more personalized treatment plans.
AI-Powered Predictive Analytics: AI in EHR can analyze patient data to predict future health risks, allowing for proactive interventions.
Clinical Decision Support: AI in EMR tools help clinicians by providing evidence-based recommendations, aiding faster, more accurate decisions.
Automated Administrative Tasks: AI reduces clinician workload by automating tasks like documentation, billing, and scheduling.
Personalized Care: AI-powered solutions in EHR systems offer tailored treatment plans based on patient history and data, improving outcomes.
Q. How do we ensure HIPAA compliance while integrating AI with EHR data?
A. Ensuring HIPAA compliance during AI integration in EHR systems is critical for maintaining patient privacy and security. AI models processing sensitive patient data must adhere to the stringent requirements of HIPAA to avoid potential legal and financial repercussions. The integration should involve proper encryption, access controls, and regular audits to guarantee compliance.
Q. How does AI reduce clinician burnout when integrated with EMRs?
A. AI in EMR systems helps reduce clinician burnout by automating time-consuming tasks and providing decision support. This not only reduces the administrative workload but also helps clinicians make faster, more accurate decisions, enhancing their efficiency.
By reducing repetitive documentation tasks, AI allows healthcare providers to focus on patient care, which ultimately improves both their well-being and job satisfaction.
Q. What are the interoperability challenges when adding AI to an existing EHR infrastructure?
A. When integrating AI and EHR systems, interoperability can pose significant challenges, especially when dealing with legacy systems. AI models may require data formats that are incompatible with older EHR systems, hindering seamless data exchange.
Ensuring that AI tools work across diverse platforms and can process data from various sources is a complex task, especially when healthcare organizations rely on different technologies.
Data format compatibility: Legacy EHR systems may not support modern data standards like FHIR or HL7, creating challenges for integration.
System integration: Older systems might lack the necessary APIs to integrate with AI-driven EHR solutions and require middleware for smooth communication.
Real-time data exchange: Ensuring that AI models in EHR systems can pull real-time data from multiple healthcare systems for accurate decision-making.
Security and compliance: Ensuring that AI solutions comply with HIPAA when exchanging EHR data across different systems.
| 2025-07-08T00:00:00 |
2025/07/08
|
https://appinventiv.com/blog/ai-in-ehr/
|
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Machine Learning in Primary Health Care: The Research Landscape
|
Machine Learning in Primary Health Care: The Research Landscape
|
https://www.mdpi.com
|
[
"Završnik",
"Kokol",
"Žlahtič",
"Blažun Vošner",
"Jernej Završnik",
"Peter Kokol",
"Bojan Žlahtič",
"Helena Blažun Vošner"
] |
Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, ...
|
The most productive funding sponsors are the National Institutes of Health, USA (= 113); the US Department of Health and Human Services (= 102); UK Research and Innovation (= 54); the European Commission (= 47); the National Institute for Health and Care Research, UK (= 46); the National Natural Science Foundation of China (36); the Ministry of Science and Technology of the People Republic of China (= 30); the Medical Research Council (= 25); and the National Institute for Aging (= 23). The rate of funded papers is 42%, which is relatively high compared to other research areas [ 37 ]. Information about the most prolific funding agencies is important because it enables research institutions to compete for grants, which could enable them to hire eminent researchers, provide access to advanced technology and research equipment, cooperate in major international scientific networks, gather new knowledge at top conferences, and/or hire leading external organizations to support the preparation of competitive project proposals.
The most prolific journals are Plos One ( n = 33), BMJ Open ( n = 26), Scientific Reports ( n = 22), Jmir Medical Informatics ( n = 19), and Journal of Medical Internet Research ( n = 15). They are prominent and recognized international journals ranked in the first quarter in their respective categories by various impact factors. Consequently, those journals present a suitable venue for researchers to find the most relevant research and publish their own research.
The most productive countries, according to the number of publications, are the United States (= 392), United Kingdom (209), India (= 107), China (= 106), Canada (= 94), Australia (= 58), Spain (= 45), Germany (= 52), the Netherlands (= 46), and South Korea (= 35). The top 10 countries all have strong economies; half of them (South Korea, Australia, Canada, the Netherlands, and Germany) are among the top 10 countries in terms of the Health Care Index [ 36 ]. All of them are ranked among the top 15 most productive countries, according to the Scimago Country Rank (Elsevier, the Netherlands), in general and medical sciences. The most productive institutions are Harvard Medical School (= 40), University of Toronto (= 30), University of Oxford (= 29), University College London (= 27), Imperial College London (= 26), and University of California, San Francisco (= 25). All the top institutions are located in the two most productive countries, namely the United States and the United Kingdom, and are among the world’s most prolific and recognized research institutions.
The search resulted in 1152 publications; among them were 838 journal articles, 165 conference papers, 67 reviews, 21 book chapters, 16 conference reports, 14 short papers, 10 editorials, and 8 other publications. Two papers were retracted. The first paper indexed in Scopus was a journal article on modelling obesity using the abductive network, published in 1997 [ 33 ]. Two papers on the use of rough sets, neural networks, and logistic regression to predict compliance in patients with coronary diseases [ 34 ] and a multiagent system for nurse and patient scheduling in primary care [ 35 ] were published in 2003 and 2005, respectively. After this ( Figure 1 ), publications were sparse, not exceeding six publications per year until 2013, when the linear growth trend began, followed by exponential growth starting in 2017 and a one-year plateau in 2022. The peak productivity was reached in 2024 with 255 publications.
In the last four years, chatbots have become more frequently used in primary health care [ 106 108 ]. They are used to make health care systems more interactive by using NLP to understand patients’ queries and give suitable responses [ 109 111 ] or even to virtualize primary health care [ 112 ], such as detecting possible COVID-19 cases and guiding patients [ 113 ]. Further examples include using chatbots to try to persuade smokers to quit smoking [ 114 ]; help patients with anxiety, depressive symptoms, or burnout syndrome [ 115 116 ]; provide support to patients with chronic diseases [ 117 ]; detect early onset of cognitive impairment [ 118 ] and suicidal intentions [ 119 ]; guide mothers or family members about breastfeeding [ 120 ]; or address patient inquiries in hospital environments [ 121 ].
The COVID-19 pandemic additionally triggered the employment of machine learning in primary health for various applications, such as the management of COVID-19 with intelligent digital health systems [ 93 ], chatbots to classify patient symptoms and recommendations of appropriate medical experts [ 94 ], the evaluation of vaccine allergy documentation [ 95 ], predicting the need for hospitalization or home monitoring of confirmed and unconfirmed coronavirus patients [ 96 ], and predicting the severity of COVID-19 among older adults [ 97 ]. From the epidemiological viewpoint, machine learning in primary health has been used for frailty identification [ 98 ], heart failure prediction [ 99 ], determining the incidence of infectious diseases from routinely collected ambulatory records [ 100 ], and identifying psychological antecedents and predictors of vaccine hesitancy [ 101 ]. On the other hand, machine learning has been used for clinical decision support for childhood asthma management [ 102 ] and predictive analytics in nursing [ 103 ]. In general, health informatics supported by machine learning can significantly improve primary health care [ 104 105 ].
Nemesure et al. [ 61 ] developed a machine learning pipeline of machine learning algorithms, including deep learning, to predict generalized anxiety disorder and major depressive disorder using data from an observational study of 4184 undergraduate students. Deep learning for automatic image analysis [ 83 ] has been used in various studies for the early diagnosis of diabetic retinopathy in diabetic patients [ 84 86 ] and predicting HER2 in bladder cancer patients [ 87 ]. Convolutional neural networks were used for the early diagnosis of multiple cardiovascular diseases [ 88 ], chronic respiratory diseases [ 89 ], or melanoma [ 90 ], reaching a high accuracy between 94% and 98%. A graph convolutional network was employed for automatic diagnosis and integrated into more than 100 hospital information systems in China to improve clinical decision-making [ 91 ]. Zhang et al. [ 92 ] developed a deep learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.
Natural language processing and big data analytics can potentially transform primary health care [ 68 69 ]. Bejan et al. [ 70 ] developed a methodology based on text mining to identify rare and severe social determinants of health in homelessness and adverse childhood experiences found in electronic health care records. Chilman et al. [ 71 ] successfully developed and evaluated a natural language processing and text mining application to analyze psychiatric clinical notes of 341,720 de-identified clinical records of a large secondary mental healthcare provider in South London to identify patients’ occupations, and Hatef et al. [ 72 ] used a similar approach on electronic health records to identify patients with high-risk housing issues. On the other hand, Scaccia [ 73 ] applied NLP to explore the concept of equity in community psychology after the COVID-19 crisis by analyzing relevant research, and Hadley et al. [ 74 ] examined the trends in health equity using text mining revenue service tax documentation submitted by nonprofit hospitals. Ford et al. [ 75 ] developed a supervised machine learning application for automated detection of patients with dementia without formal diagnosis, using routinely collected electronic health records to improve service planning and delivery of quality care. Kasthurirathne et al. [ 76 ] used random forest machine learning and NLP algorithms on integrated patient clinical data and community-level data representing patients’ social determinants of health obtained from multiple sources to build models to predict the need for referral to mental health professionals, dietitians, social workers, or other SDH services. Big data analysis using traditional non-text clinical data was used to recognize patterns of collaboration between physicians, nurses, and dietitians in the treatment of patients with type 2 diabetes mellitus; compare these patterns with the clinical evolution of the patients within the context of primary care; determine patterns that lead to the improved treatment of patients [ 77 ]; classify skin diseases [ 78 ]; predict the influx of patients to primary health centers [ 79 ]; and predict high-risk pregnancies early [ 80 ]. Garies et al. [ 81 ] used machine learning to derive health-related social determinants of primary care patients. On a larger scale, AI was used to derive social determinants of health data from medical records in Canada [ 82 ].
The use of machine learning in primary health care has recently gained popularity and promise [ 26 50 ]. Pikoula et al. [ 51 ] and Jennings et al. [ 52 ] used clustering, correspondence analysis, and decision trees on medical record data from 30961 smokers diagnosed with COPD to classify them into groups with differing risk factors, comorbidities, and prognoses. In general, AI is often used in managing COPD [ 53 ]. Oude et al. [ 54 ] developed a clinical decision support system based on various decision tree algorithms for self-referral of patients with low back pain to prevent their transition into chronic back pain. In general, AI is frequently used to support services for patients with musculoskeletal diseases [ 55 ]. Sekelj et al. [ 56 ] and performed a study to evaluate the ability of machine learning algorithms to identify patients at high risk of atrial fibrillation in primary care. They found that the algorithm performed in a way that, if implemented in practice, could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening. Similarly, Norman et al. [ 57 ] used machine learning to predict new cases of hypertension. Liu et al. found that machine learning-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes. On an epidemiological level, new diabetes patients were identified using stochastic gradient boosting [ 58 ]. Priya and Thilagamani [ 59 ] developed a machine learning-based prediction model to predict arterial stiffness risk in diabetes patients. Machine learning has also been used for the prediction/classification of infectious diseases [ 6 60 ], anxiety [ 61 ], COVID-19 severity [ 62 ], cancer [ 24 63 ], or even patient no-shows [ 13 64 ]. On the other hand, Evans et al. [ 65 ], Fong [ 66 ], and Govender [ 67 ] developed an automated classification of patient safety reports system using machine learning.
Maclagam et al. [ 38 39 ] used natural language processing of free texts in electronic health records and clinical notes to identify patients with risk of dementia, Alzheimer’s, or cognitive impairment [ 40 ] in a preventive manner to shorten the length of hospitalization, delay admission to long-term care, and reduce the number of underrecognized patients with the above diseases. Artificial intelligence and speech and language processing have been used to predict the occurrence of Alzheimer’s disease [ 41 ] or cognitive decline in the context of aging to facilitate restorative and preventive treatments [ 42 47 ].
The publications from the corpus were analyzed using VOSviewer software (Steps 3 and 4 of the SKS framework). Text mining identified 1861 author keywords, and according to Zipf’s law, 83 were selected for the bibliometric mapping analysis. The resulting author keyword landscape is shown in Figure 2 . Altogether, fourteen categories and six research themes were identified, as shown in Table 1
3.3. Deductive Synthetic Knowledge Synthesis
The deductive part of the SKS analysis revealed that the benefits of using machine learning in primary health care emerge at three beneficiary levels: the patient level, the health care provider level, and the health care system level. Potential patient benefits include improved quality of life, patient-centered care and patient safety, early diagnosis, identification of high-risk patients, screening effectiveness, and more effective and efficient prevention and treatment of diseases. The most targeted diseases mentioned in over five publications were COVID-19, dementia, cardiovascular diseases, depression, diabetes, Alzheimer’s, asthma, suicide, mental health, mild cognitive impairment, and cancer. Potential benefits for health care providers include facilitated referrals, enhanced quality of primary health delivery, better communications, and reduced workload. Potential benefits for health care systems include enhanced population-based screening, surveillance, predictions, more effective and efficient decision-making on the system level, better management of health institutions, and reduced economic burden.
The most frequently used machine learning approaches were deep learning, decision trees, logistic regression, convolutional neural networks, neural networks, and random forests.
A comparison of the 2022–2023 and 2024–2025 research landscapes revealed that the content of the research on machine learning in primary health care has not changed much in recent years. However, the focus shifted from COVID-19, Alzheimer’s, digital health transformation, classification and decision support, big data, eHealth, and convolutional networks to natural language processing, chatbots, cardiology, general practice, quality improvement on an individual level, and computer vision. The shift in focus might have occurred due to the advancement in natural language processing. Earlier AI focused more on structured data (like medical images or big data analytics); however, the advent of large language models enabled the potential for AI to understand and generate human language. This makes chatbots and conversational AI much more viable for patient interaction, administrative tasks, and providing basic health information. COVID-19 and Alzheimer’s were significant research priorities due to their global impact and the urgent need for solutions. AI’s early promise in analyzing vast datasets for drug discovery, vaccine development, or disease prediction made it a natural fit. However, unique challenges, including high patient volumes, diverse conditions, and the need for efficient triage and decision support, triggered the development of AI applications directly applicable to common primary care scenarios and the integration of AI into the daily workflow of primary care providers. An additional reason for the shift might be the emergence of user-centric designs and the growing understanding that AI tools need to be practical and user-friendly for health care professionals. In essence, the shift signifies a move from exploring the broad potential of AI in health care to focusing on more targeted, mature, and practically applicable solutions that address the specific needs and challenges of primary health care.
SKS also identified some challenges for the successful and widespread use of machine learning in primary health care, such as how to more actively involve end users; how to make a paradigm shift from technology-centered to human-centered design approaches; how to ensure cost-effectiveness and performance of machine learning-based primary health care systems; how to overcome ethical, standardization, and legal aspects (i.e., data protection and security); how to increase the AI health literacy of patients; and finally, how to validate the quality and validity of the input data for machine learning algorithm training. Among these challenges, the ethical and regulatory barriers might be the hardest to overcome. If the training datasets are not representative of diverse populations or are, for example, prioritizing cost-saving measures, machine learning can perpetuate and even amplify existing societal biases and health disparities. Another concern might be that machine learning can inadvertently “memorize” sensitive health information, which can lead to erosion of trust in patient–provider relationships. Finally, machine learning might diminish the human elements of health care, reducing empathy in patient–health care professional interactions.
| 2025-01-14T00:00:00 |
2025/01/14
|
https://www.mdpi.com/2227-9032/13/13/1629
|
[
{
"date": "2022/12/01",
"position": 94,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 93,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 94,
"query": "artificial intelligence healthcare"
}
] |
Leading-Edge Care: Where High Tech Meets High Touch
|
Journal of Healthcare Management
|
https://journals.lww.com
|
[
"Rissmiller",
"Author Information"
] |
Healthcare delivery is changing fast, with seemingly endless new artificial intelligence (AI) platforms, new virtual options, new devices ...
|
You can read the full text of this article if you:
Select an option Log In Buy Article Society Membership Content & Permissions Access through Ovid
| 2025-07-03T00:00:00 |
2025/07/03
|
https://journals.lww.com/jhmonline/fulltext/2025/07000/leading_edge_care__where_high_tech_meets_high.3.aspx
|
[
{
"date": "2022/12/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 86,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 95,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 97,
"query": "artificial intelligence healthcare"
}
] |
Unlocking the Power of Data: How Databricks, WashU & Databasin ...
|
Unlocking the Power of Data: How Databricks, WashU & Databasin Are Redefining Healthcare Innovation
|
https://www.databricks.com
|
[] |
Washington University School of Medicine has partnered with Databricks and Databasin to build a modern, AI-ready data infrastructure.
|
What happens when one of the nation’s top academic medical centers teams up with the leading data and AI platform in the world? You get a blueprint for transforming healthcare — one breakthrough at a time.
For more than three years, Washington University School of Medicine has partnered with Databricks and Databasin to build a modern, AI-ready data infrastructure.
In a candid Q&A, Dr. Philip Payne (Chief Health AI Officer at WashU Medicine and BJC) and Mike Sanky (Global Industry Lead at Databricks) reflect on the impact of this partnership — from uncovering Alzheimer’s “treatment deserts” in St. Louis to accelerating the use of generative AI in hospital admissions.
| 2025-07-07T00:00:00 |
2025/07/07
|
https://www.databricks.com/blog/unlocking-power-data-how-databricks-washu-databasin-are-redefining-healthcare-innovation
|
[
{
"date": "2022/12/01",
"position": 99,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 96,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/02/01",
"position": 97,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 98,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 98,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/10/01",
"position": 99,
"query": "artificial intelligence healthcare"
},
{
"date": "2025/01/01",
"position": 99,
"query": "artificial intelligence healthcare"
}
] |
AI and the Future of News - Reuters Institute - University of Oxford
|
AI and the Future of News
|
https://reutersinstitute.politics.ox.ac.uk
|
[] |
This report looks at how people use generative AI, and what they think about its application in journalism and other areas of work and life across six countries ...
|
Overview
AI is central to the Reuters Institute for the Study of Journalism’s mission to explore the future of journalism worldwide. Since 2016, we have worked with journalists and editors, technologists, and others to better understand what the development of artificial intelligence might mean for the future of news, and published research on various aspects of this.
Here you can find more information about what we are doing around AI for practising journalists covering these technologies and the issues they raise, for editors and news media executives navigating what they might mean for the industry, and for everyone interested in learning more about AI and the future of news from our research and our original reporting. | Learn more | Sign up for updates on our AI work
| 2022-12-01T00:00:00 |
https://reutersinstitute.politics.ox.ac.uk/ai-journalism-future-news
|
[
{
"date": "2022/12/01",
"position": 2,
"query": "artificial intelligence journalism"
},
{
"date": "2023/01/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/02/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/03/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/03/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/04/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/05/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/07/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/07/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/09/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/10/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/10/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/11/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2023/12/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2024/02/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2024/02/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2024/05/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2024/06/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2024/06/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2024/07/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2024/09/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2024/09/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2024/11/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2025/01/01",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2025/02/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2025/02/01",
"position": 2,
"query": "artificial intelligence journalism"
},
{
"date": "2025/03/01",
"position": 2,
"query": "artificial intelligence journalism"
},
{
"date": "2025/05/01",
"position": 2,
"query": "AI journalism"
},
{
"date": "2025/05/01",
"position": 2,
"query": "artificial intelligence journalism"
}
] |
|
JournalismAI
|
JournalismAI
|
https://www.journalismai.info
|
[] |
JournalismAI is a global initiative that empowers news organisations to use artificial intelligence responsibly. · What we do · Where we work · Get involved.
|
Our programmes, largely run virtually, are designed for all journalists interested in getting to grips with AI and journalism. We have trained over 3,000 journalists around the world.
| 2022-12-01T00:00:00 |
https://www.journalismai.info/
|
[
{
"date": "2022/12/01",
"position": 6,
"query": "artificial intelligence journalism"
},
{
"date": "2023/01/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/02/01",
"position": 10,
"query": "artificial intelligence journalism"
},
{
"date": "2023/03/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/03/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2023/04/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2023/05/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2023/07/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/07/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2023/09/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/09/20",
"position": 2,
"query": "artificial intelligence journalism"
},
{
"date": "2023/10/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/10/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2023/11/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2023/12/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2024/02/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2024/02/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2024/05/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2024/06/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2024/06/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2024/07/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2024/09/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2024/09/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2024/11/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2025/01/01",
"position": 5,
"query": "artificial intelligence journalism"
},
{
"date": "2025/02/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2025/02/01",
"position": 4,
"query": "artificial intelligence journalism"
},
{
"date": "2025/03/01",
"position": 4,
"query": "artificial intelligence journalism"
},
{
"date": "2025/05/01",
"position": 1,
"query": "AI journalism"
},
{
"date": "2025/05/01",
"position": 5,
"query": "artificial intelligence journalism"
}
] |
|
Use of AI in Journalism - Radio Television Digital News Association
|
Radio Television Digital News Association
|
https://www.rtdna.org
|
[] |
AI (Artificial Intelligence) can have a role in ethical, responsible and truthful journalism. However, it should not be used to replace human judgment and ...
|
If news organizations are going to use AI, RTDNA recommends they have a clear policy for how AI is to be used in newsgathering, editing and distributing content across platforms. AI intersects with core journalism principles like accuracy, context, trust, and transparency. Carefully weigh all issues before integrating into your news organization. Because this is an emerging and fast-changing area, newsrooms and RTDNA might find it necessary to review guidelines regularly.
Below is an outline of critical areas of focus and questions you should consider when drafting a policy:
Accuracy, Context and Clarity
AI programs have the ability to modify every element of content — audio, video, still pictures, and words. In many cases, AI programs may enhance your media. However, AI programs may not offer the proper context, have facts misplaced or may be confusing to the end user without thoughtful guidelines.
The following questions should help you guide your decision-making around accuracy, context, and clarity:
What do your newsroom/station/parent company guidelines say about AI use surrounding accuracy, context, and clarity? Are they up to date?
Can you fully understand the capabilities and source material for the AI program before implementation? Also consider: What are your safeguards to protect against inadvertent plagiarism? Can you independently verify the AI tool’s accuracy? Are there opportunities to test the AI tool prior to publication?
How have you taken ownership over the disclosure language for the consumer?
What is your newsroom system and set of expectations for human review before publication?
Transparency and Disclosure
In establishing policies around the use of artificial intelligence in newsrooms, consider the importance of transparency to the trust you build with your audience.
In general, disclosing how you use artificial intelligence is preferable to non-disclosure.
The following questions should help you guide your decision-making around transparency and AI use: • Does the benefit to the public of your use of AI outweigh any risk or detriment to trust in news by not disclosing its use?
How does your audience feel about the use of this technology?
What falls under the definition of AI? Examples: content generation vs. content distribution/organization vs. video or text editing vs. grammar/spelling tools.
Where will you provide disclosure about your use of AI? Examples: Website, social media, on-air
Are journalists able to review any and all AI-influenced content before it reaches the consumer? If not, can you justify its use to the audience?
Privacy
Journalists have long-standing practices to weigh the public’s right to know against an individual’s right to privacy. It is unlikely AI can properly consider these issues in the same way. It is important to ensure that AI is programmed to operate within ethical and legal boundaries and that its use does not violate privacy or other fundamental rights.
| 2022-12-01T00:00:00 |
https://www.rtdna.org/use-of-ai-in-journalism
|
[
{
"date": "2022/12/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2023/02/01",
"position": 8,
"query": "artificial intelligence journalism"
},
{
"date": "2023/03/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2023/04/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2023/05/16",
"position": 1,
"query": "artificial intelligence journalism"
},
{
"date": "2023/06/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2023/07/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2023/10/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2024/02/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2024/05/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2024/06/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2024/09/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2024/11/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2025/01/01",
"position": 9,
"query": "artificial intelligence journalism"
},
{
"date": "2025/02/01",
"position": 8,
"query": "artificial intelligence journalism"
},
{
"date": "2025/03/01",
"position": 8,
"query": "artificial intelligence journalism"
},
{
"date": "2025/05/01",
"position": 8,
"query": "artificial intelligence journalism"
}
] |
|
JournalismAI - LSE
|
JournalismAI
|
https://www.lse.ac.uk
|
[
"London School Of Economics",
"Political Science"
] |
JournalismAI is a global initiative that aims to inform media organisations about the potential offered by AI-powered technologies.
|
JournalismAI
JournalismAI is a global initiative that empowers news organisations to use artificial intelligence responsibly. We support innovation and capacity-building in news organisations to make the potential of AI more accessible and to counter inequalities in the global news media around AI. JournalismAI is a project of Polis – the LSE's journalism think-tank – and is supported by the Google News Initiative.
| 2022-12-01T00:00:00 |
https://www.lse.ac.uk/media-and-communications/polis/JournalismAI
|
[
{
"date": "2022/12/01",
"position": 13,
"query": "artificial intelligence journalism"
},
{
"date": "2023/01/01",
"position": 5,
"query": "AI journalism"
},
{
"date": "2023/03/01",
"position": 5,
"query": "AI journalism"
},
{
"date": "2023/03/01",
"position": 12,
"query": "artificial intelligence journalism"
},
{
"date": "2023/05/01",
"position": 4,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 3,
"query": "AI journalism"
},
{
"date": "2023/06/01",
"position": 12,
"query": "artificial intelligence journalism"
},
{
"date": "2023/07/01",
"position": 5,
"query": "AI journalism"
},
{
"date": "2023/07/01",
"position": 12,
"query": "artificial intelligence journalism"
},
{
"date": "2023/09/01",
"position": 7,
"query": "AI journalism"
},
{
"date": "2023/10/01",
"position": 5,
"query": "AI journalism"
},
{
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|
10 Things You Should Know About AI in Journalism
|
10 Things You Should Know About AI in Journalism
|
https://gijn.org
|
[] |
10 essential points that reporters should know about AI in journalism — such as how it can help news organizations, why he thinks we should stop calling it AI ...
|
Editor’s Note: Mattia Peretti manages JournalismAI — a research and training project at Polis, the international journalism think tank of the London School of Economics — which helps news organizations use artificial intelligence (AI) more responsibly. At a Media Party innovation conference in Buenos Aires, he shared 10 essential points that reporters should know about AI in journalism — such as how it can help news organizations, why he thinks we should stop calling it AI, and the need to hold accountable the people and systems behind it when it’s used irresponsibly.
Here are 10 things I learned, and 10 things you also should know, about AI in journalism:
1. AI Is Not What You Think
First of all: AI is probably not what you think. We have been biased by science fiction to form in our brains images of robots and dystopian futures where they fight with us for the control of the universe. But the “AI” we are talking about in the context of journalism is more similar to a spreadsheet than to any kind of robot.
With “AI,” we refer to “a collection of ideas, technologies, and techniques that relate to a computer system’s capacity to perform tasks that normally require human intelligence.”
There is an important distinction that you should keep in mind: The AI that currently exists is Artificial Narrow Intelligence. Computer programs that can perform a single task extremely well, even better than us. The AI depicted by science fiction is Artificial General Intelligence, and that’s nothing more than an idea right now. A hope for someone. The idea that machines can be made to think and function as the human mind.
All in all, I may go as far to say that it would be better if we all stopped calling it AI because of the misconceptions that these two letters inevitably create when they come together. Any time you read “AI” — or you are writing “AI” in your articles if you are a journalist — stop for a second to think what other word could replace it in that sentence. Maybe it’s an algorithm, or automation, or a computer program. That little replacement exercise will help your understanding, and your readers too.
And it’s not only about words either. AI as a field is deeply misrepresented in images as well. Because if you type “artificial intelligence” in your search engine, you get something like this:
Humanoid robots, glowing brains, outstretched robot hands, and sometimes the Terminator. These images fuel misconceptions about AI and set unrealistic expectations on its capabilities. Better Images of AI decided to do something about it. It’s a non-profit initiative that is researching the issue and curating a repository of “better” images to represent AI.
2. AI Is Not Stealing Your Job
AI is not stealing your job. Read this out loud: “AI is not stealing my job.” Let that sink in.
The truth is that artificial intelligence is not nearly as intelligent as it would need to be to replace you. It can take away some tasks that we normally do. But it’s us who decide what those tasks are, based on what tools we decide to build with AI.
It’s not a coincidence that, in the context of journalism, AI currently does mostly boring and repetitive tasks that we don’t really enjoy doing anyway. Things like transcribing interviews, sifting through hundreds and hundreds of leaked documents, filtering reader comments comments, writing the same story about companies’ financial earnings, for hundreds of companies, every three months. No one got into journalism because they were looking forward to doing those things again and again.
AI can do many things that can support your work and, as any other technological innovation before, it is changing newsroom roles. But then it’s up to us. We decide what to ask AI to do. By itself it doesn’t have the ambition, nor the ability to steal our jobs anytime soon.
3. You May Not Need AI
My goal working on JournalismAI is not to tell you that you should use AI. The mission of my team is to ask what problem you are trying to solve, and help you figure out if AI can be part of the solution.
Honestly, often it is not. Because you may be able to solve your challenges or achieve your goals with other tools that are easier to use, less expensive, and more secure.
Do not get into the AI space for FOMO, for fear of missing out. Do not give in to all the fancy headlines telling you that AI will revolutionize journalism or even “save it,” because it will not.
Do you want to use AI? Tell me your use case first.
4. You Need A Strategy
Now, let’s say you have done your homework. You analyzed your use case, understood what AI can and cannot do, and you reached the conclusion that indeed using AI could help you. It’s not time to celebrate yet because I have to warn you: Implementing AI can be hard. You need to think strategically.
Sometimes we talk about “implementing AI” as if it was one single process. But the reality is that depending on your use case and the type of technology (AI is an umbrella term with many subfields), that process may be completely different from the one you have to go through for a different use case.
Using a machine learning model to filter the comments you receive from your readers, automatically tagging potentially harmful ones, for example, requires a completely different process than if you want to optimize your paywall to maximize your chances to turn a sporadic reader into a paying subscriber.
Understand your use case and design an implementation strategy that is specific to your use case as well as to the strengths and weaknesses of your organization.
5. Tools: Buy, Build, Or…
“Using AI” means using some technological tools that can help your work as journalists. So the question is, where do you get those tools?
“The secret is that buy vs build is a false dichotomy.” — Mattia Peretti, JournalismAI
The debate in the industry lies mostly around the dichotomy buy vs build: Do you build AI tools in house to serve your specific purpose or do you instead buy existing tools and adapt them to your needs? It’s a hard choice and it’s not that one option is right and the other is wrong.
Famously, two international news agencies that have been at the forefront of AI innovation have taken different approaches: Reuters builds most of its AI tools in house, while the AP buys tools by working with startups and vendors in the open market.
Making a full list of pros and cons would take too long, but what I want you to understand is that, once again, you have to find your own way. You must consider what you are trying to do, evaluate the costs of both options, consider carefully what skills you already have in your team or what you can easily acquire, and chart a path that works for you.
The good news is that you are not alone. If you decide to build, it’s likely that there are others in the industry who went through that route already and that you can learn from. And if you decide to buy existing tools, there are many available out there and not all of them are as expensive as you may think.
But the secret is that buy vs build is a false dichotomy.
There’s a third route, which is to partner. With startups or academic labs that may have the skills you need, may have done a lot of useful research already, and could be eager to partner with you to apply their theoretical learnings to a practical use case.
“Journalism is a small industry compared to many others that are more advanced in the use of AI.” — Mattia Peretti, JournalismAI
A good way to begin this journey is to have a look at the JournalismAI Starter Pack, an interactive source that allows you to explore what AI might do for your journalism.
6. Unicorns Exist
This point is actually two points but they are both about talent.
One of the biggest challenges for news organizations that are getting into AI is finding or acquiring the technical skills they need to implement their AI-related projects. It’s hard because, let’s face it, journalism is a small industry compared to many others that are more advanced in the use of AI. And we don’t have loads of money to throw at computer scientists and engineers either.
Journalism does have something that not all industries can offer, though. We are a mission-driven industry where work is done not just for profit but for the betterment of society. This matters to people. Not to everyone, but to many.
You have to leverage your mission. You won’t convince technical people to work for you by outbidding companies in other industries or by offering other perks. Your mission is what you have to sell. Focus on attracting people who want a job that offers them a purpose and not just profit.
The second part of this point here is that you don’t need to have “AI” in your job title to work on AI projects. Through our fellowship programs and collaborative initiatives, I learned that what AI projects need more than anything are interdisciplinary teams. People from editorial, product, and technical departments working together make the best AI projects.
And you can train to be part of this too. There are lots of free courses online — some more technical, others more strategic — that you can take to learn more about AI, maybe learn to code if that’s what you aspire to, and build up your skills so that you can get a seat at the table. You can be part of that interdisciplinary team working on the next AI project in your organization.
7. Collaboration is Essential
The interdisciplinary teams we just mentioned are one layer of collaboration that your AI projects can benefit from. Same for the idea of partnering with startups and academic labs to help your projects come to life. But there’s more.
Someone in another news organization is facing your same challenges and may be trying to build the exact same thing you have in mind. Maybe they are your direct competitors or maybe they are on the other side of the planet, it doesn’t matter. When it comes to AI, collaboration is your biggest asset. You can decide to do it on your own, but unless you have the resources of the New York Times, it is likely that you’ll be left behind.
For three years now, my team has been helping news organizations from across the world collaborate on AI projects and I can tell you that it’s possible. It’s not easy, it takes effort and trust, but it’s possible and absolutely worth it.
From using computer vision in combination with satellite imagery to power your investigative reporting on environmental issues, to using AI tools to track the impact of influencers on our society, or to detect examples of hate speech directed at journalists and activists, our fellows and participants have been doing incredible work with AI by embracing cross-border collaboration and you should consider it too if you are not already doing it.
8. You Need A Space to Experiment
“There is so much progress in these models, and so quickly, that it’s hard to keep up.” That’s what an expert in our network told me in a recent conversation. And they are right. Things change very quickly in the field of AI.
New tools are released seemingly every other day, new scientific papers are published daily. Trying to keep up with everything is just not realistic. But that shouldn’t be your ambition either. Keep your ears wide open for interesting developments but be skeptical of what you hear and learn to recognize what you should truly pay attention to. You don’t need to test every single fancy tool that it’s released.
“AI is becoming more and more present in every part of society. And often it’s not used in a responsible way, with disastrous consequences on certain segments of population. The world needs journalists to report on AI and hold these systems and the people behind them accountable.” — Mattia Peretti, JournalismAI
You need to design a strategy that is flexible and adaptive in order to navigate such a fast-evolving space. The first step should be to build a space in your organization or join opportunities outside your organization (like the ones we offer) where you can safely experiment with AI without the pressure of immediate delivery.
Your AI team or your AI people must be given a safe space and the time to fail and learn from failure.
9. Understand AI to Report On It
AI is becoming more and more present in every part of society. And often it’s not used in a responsible way, with disastrous consequences on certain segments of population. The world needs journalists to report on AI and hold these systems and the people behind them accountable.
There are more and more journalists doing fantastic work on algorithmic accountability, and initiatives like the AI Accountability Network of the Pulitzer Center are allowing more reporters to build their skills for this critical purpose.
What I learned is that this can be done best once you understand how AI works via direct experience. Teams like the AI and Automation Lab of Bavarian Radio in Germany are positioning themselves as leaders in this field because they have been smart enough to recognize this, creating a team, a dedicated lab, that is responsible for both using AI for product development and for algorithmic accountability reporting.
You can report on AI much better if you know how to use it — and you will be much more responsible in using it if you understand the risks and potential consequences.
10. A Transformational Opportunity
After almost four years working on JournalismAI, I am convinced that we have a transformational opportunity. I’m not thinking of the examples of automation we listed earlier, although I recognize the efficiency they provide.
I’m referring to the opportunity to use AI to gather the insights we need to make our journalism better. Creative and inspiring ideas to use data and AI to better understand what we produce and how we do it.
From using AI to understand, identify, and mitigate existing biases in our newsrooms, to a project in our current fellowship that aims to use NLP (Natural Language Processing) tools to analyze a publication’s content and spot underreported topics to make their coverage more inclusive. And even rethinking the core journalistic product, atomizing the article to create innovative storytelling formats that are more focused on modern user needs.
These are opportunities to use technology to transform our journalism into something that is more diverse, inclusive, and responsible. That’s why I’m excited about AI. Because while it saves journalists some time and makes their work more efficient, it frees up time to use the skills that truly make us humans, like empathy and creativity.
To wrap it up, I invite you to be balanced in approaching AI: Be skeptical, understand the limitations of the technology and its risks, and be careful and responsible once you decide to use it. But while you do all of that, allow yourself to get excited for the opportunities that it opens up.
It’s on us to decide if we want to sit back and wait for AI to impact our journalism in ways we don’t understand, or if we want to own it and use AI to make a positive impact in our societies.
This post was originally published on the London School of Economics’ Polis blog and and is reprinted here with permission.
Additional Resources
AI Journalism Lessons from a 150-Year-Old Argentinian Newspaper
Journalists’ Guide to Using AI and Satellite Imagery for Storytelling
Deepfake Geography: How AI Can Now Falsify Satellite Images
| 2022-12-01T00:00:00 |
https://gijn.org/stories/10-things-you-should-know-about-ai-in-journalism/
|
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