Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unremarkable AI: Fitting Intelligent Decision Support into Critical,
  Clinical Decision-Making Processes   Clinical decision support tools (DST) promise improved healthcare outcomes by
offering data-driven insights. While effective in lab settings, almost all DSTs
have failed in practice. Empirical research diagnosed poor contextual fit as
the cause. This paper describes the design and field evaluation of a radically
new form of DST. It automatically generates slides for clinicians' decision
meetings with subtly embedded machine prognostics. This design took inspiration
from the notion of "Unremarkable Computing", that by augmenting the users'
routines technology/AI can have significant importance for the users yet remain
unobtrusive. Our field evaluation suggests clinicians are more likely to
encounter and embrace such a DST. Drawing on their responses, we discuss the
importance and intricacies of finding the right level of unremarkableness in
DST design, and share lessons learned in prototyping critical AI systems as a
situated experience.

9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
2
The categories of this paper are: math
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex