Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness.

Abstract:

:The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..

journal_name

Schizophr Bull

journal_title

Schizophrenia bulletin

authors

Chandler C,Foltz PW,Elvevåg B

doi

10.1093/schbul/sbz105

subject

Has Abstract

pub_date

2020-01-04 00:00:00

pages

11-14

issue

1

eissn

0586-7614

issn

1745-1701

pii

5611057

journal_volume

46

pub_type

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