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dc.contributor.authorChandler, Chelsea
dc.contributor.authorFoltz, Peter W.
dc.contributor.authorElvevåg, Brita
dc.date.accessioned2021-04-19T07:26:30Z
dc.date.available2021-04-19T07:26:30Z
dc.date.issued2019-11-01
dc.description.abstractThe 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.en_US
dc.descriptionThis is a pre-copyedited, author-produced version of an article accepted for publication in <i>Schizophrenia Bulletin.</i> following peer review. The version of record Chandler, C., Foltz, P.W. & Elvevåg, B. (2020). Using machine learning in psychiatry: The need to establish a framework that nurtures trustworthiness. <i>Schizophrenia Bulletin, 46</i>(1), 11-14 is available online at: <a href=https://doi.org/10.1093/schbul/sbz105>https://doi.org/10.1093/schbul/sbz105</a>.en_US
dc.identifier.citationChandler, C., Foltz, P.W. & Elvevåg, B. (2020). Using machine learning in psychiatry: The need to establish a framework that nurtures trustworthiness. <i>Schizophrenia Bulletin, 46</i>(1), 11-14.en_US
dc.identifier.cristinIDFRIDAID 1819435
dc.identifier.doi10.1093/schbul/sbz105
dc.identifier.issn0586-7614
dc.identifier.issn1745-1701
dc.identifier.urihttps://hdl.handle.net/10037/20919
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.relation.journalSchizophrenia Bulletin
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Psychiatry, child psychiatry: 757en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Psykiatri, barnepsykiatri: 757en_US
dc.titleUsing machine learning in psychiatry: The need to establish a framework that nurtures trustworthinessen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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