Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: When will the mist clear? On the Interpretability of Machine Learning
  for Medical Applications: a survey   Artificial Intelligence is providing astonishing results, with medicine being
one of its favourite playgrounds. In a few decades, computers may be capable of
formulating diagnoses and choosing the correct treatment, while robots may
perform surgical operations, and conversational agents could interact with
patients as virtual coaches. Machine Learning and, in particular, Deep Neural
Networks are behind this revolution. In this scenario, important decisions will
be controlled by standalone machines that have learned predictive models from
provided data. Among the most challenging targets of interest in medicine are
cancer diagnosis and therapies but, to start this revolution, software tools
need to be adapted to cover the new requirements. In this sense, learning tools
are becoming a commodity in Python and Matlab libraries, just to name two, but
to exploit all their possibilities, it is essential to fully understand how
models are interpreted and which models are more interpretable than others. In
this survey, we analyse current machine learning models, frameworks, databases
and other related tools as applied to medicine - specifically, to cancer
research - and we discuss their interpretability, performance and the necessary
input data. From the evidence available, ANN, LR and SVM have been observed to
be the preferred models. Besides, CNNs, supported by the rapid development of
GPUs and tensor-oriented programming libraries, are gaining in importance.
However, the interpretability of results by doctors is rarely considered which
is a factor that needs to be improved. We therefore consider this study to be a
timely contribution to the issue.

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The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys