Do as AI say: susceptibility in deployment of clinical decision-aids

Abstract Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were...

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Autores principales:	Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, Marzyeh Ghassemi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/fbdca284c3614633abe5587e5294e051
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spelling	oai:doaj.org-article:fbdca284c3614633abe5587e5294e0512021-12-02T14:21:51ZDo as AI say: susceptibility in deployment of clinical decision-aids10.1038/s41746-021-00385-92398-6352https://doaj.org/article/fbdca284c3614633abe5587e5294e0512021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00385-9https://doaj.org/toc/2398-6352Abstract Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.Susanne GaubeHarini SureshMartina RaueAlexander MerrittSeth J. BerkowitzEva LermerJoseph F. CoughlinJohn V. GuttagErrol ColakMarzyeh GhassemiNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Susanne Gaube Harini Suresh Martina Raue Alexander Merritt Seth J. Berkowitz Eva Lermer Joseph F. Coughlin John V. Guttag Errol Colak Marzyeh Ghassemi Do as AI say: susceptibility in deployment of clinical decision-aids
description	Abstract Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.
format	article
author	Susanne Gaube Harini Suresh Martina Raue Alexander Merritt Seth J. Berkowitz Eva Lermer Joseph F. Coughlin John V. Guttag Errol Colak Marzyeh Ghassemi
author_facet	Susanne Gaube Harini Suresh Martina Raue Alexander Merritt Seth J. Berkowitz Eva Lermer Joseph F. Coughlin John V. Guttag Errol Colak Marzyeh Ghassemi
author_sort	Susanne Gaube
title	Do as AI say: susceptibility in deployment of clinical decision-aids
title_short	Do as AI say: susceptibility in deployment of clinical decision-aids
title_full	Do as AI say: susceptibility in deployment of clinical decision-aids
title_fullStr	Do as AI say: susceptibility in deployment of clinical decision-aids
title_full_unstemmed	Do as AI say: susceptibility in deployment of clinical decision-aids
title_sort	do as ai say: susceptibility in deployment of clinical decision-aids
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fbdca284c3614633abe5587e5294e051
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Do as AI say: susceptibility in deployment of clinical decision-aids

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