Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Future AI in Healthcare: A Tsunami of False Alarms or a Product of
  Experts?   Recent significant increases in affordable and accessible computational power
and data storage have enabled machine learning to provide almost unbelievable
classification and prediction performances compared to well-trained humans.
There have been some promising (but limited) results in the complex healthcare
landscape, particularly in imaging. This promise has led some individuals to
leap to the conclusion that we will solve an ever-increasing number of problems
in human health and medicine by applying `artificial intelligence' to `big
(medical) data'. The scientific literature has been inundated with algorithms,
outstripping our ability to review them effectively. Unfortunately, I argue
that most, if not all of these publications or commercial algorithms make
several fundamental errors. I argue that because everyone (and therefore every
algorithm) has blind spots, there are multiple `best' algorithms, each of which
excels on different types of patients or in different contexts. Consequently,
we should vote many algorithms together, weighted by their overall performance,
their independence from each other, and a set of features that define the
context (i.e., the features that maximally discriminate between the situations
when one algorithm outperforms another). This approach not only provides a
better performing classifier or predictor but provides confidence intervals so
that a clinician can judge how to respond to an alert. Moreover, I argue that a
sufficient number of (mostly) independent algorithms that address the same
problem can be generated through a large international competition/challenge,
lasting many months and define the conditions for a successful event. Finally,
I propose introducing the requirement for major grantees to run challenges in
the final year of funding to maximize the value of research and select a new
generation of grantees.

17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th