Presenting machine learning model information to clinical end users with model facts labels

There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective p...

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Autores principales:	Mark P. Sendak, Michael Gao, Nathan Brajer, Suresh Balu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/04a32595eab3442b8044b66e5a84a0f7
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id	oai:doaj.org-article:04a32595eab3442b8044b66e5a84a0f7
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spelling	oai:doaj.org-article:04a32595eab3442b8044b66e5a84a0f72021-12-02T17:04:38ZPresenting machine learning model information to clinical end users with model facts labels10.1038/s41746-020-0253-32398-6352https://doaj.org/article/04a32595eab3442b8044b66e5a84a0f72020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0253-3https://doaj.org/toc/2398-6352There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective presents the “Model Facts” label, a systematic effort to ensure that front-line clinicians actually know how, when, how not, and when not to incorporate model output into clinical decisions. The “Model Facts” label was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in 1-page. Practitioners and regulators must work together to standardize presentation of machine learning model information to clinical end users in order to prevent harm to patients. Efforts to integrate a model into clinical practice should be accompanied by an effort to clearly communicate information about a machine learning model with a “Model Facts” label.Mark P. SendakMichael GaoNathan BrajerSuresh BaluNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-4 (2020)
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 Mark P. Sendak Michael Gao Nathan Brajer Suresh Balu Presenting machine learning model information to clinical end users with model facts labels
description	There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective presents the “Model Facts” label, a systematic effort to ensure that front-line clinicians actually know how, when, how not, and when not to incorporate model output into clinical decisions. The “Model Facts” label was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in 1-page. Practitioners and regulators must work together to standardize presentation of machine learning model information to clinical end users in order to prevent harm to patients. Efforts to integrate a model into clinical practice should be accompanied by an effort to clearly communicate information about a machine learning model with a “Model Facts” label.
format	article
author	Mark P. Sendak Michael Gao Nathan Brajer Suresh Balu
author_facet	Mark P. Sendak Michael Gao Nathan Brajer Suresh Balu
author_sort	Mark P. Sendak
title	Presenting machine learning model information to clinical end users with model facts labels
title_short	Presenting machine learning model information to clinical end users with model facts labels
title_full	Presenting machine learning model information to clinical end users with model facts labels
title_fullStr	Presenting machine learning model information to clinical end users with model facts labels
title_full_unstemmed	Presenting machine learning model information to clinical end users with model facts labels
title_sort	presenting machine learning model information to clinical end users with model facts labels
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/04a32595eab3442b8044b66e5a84a0f7
work_keys_str_mv	AT markpsendak presentingmachinelearningmodelinformationtoclinicalenduserswithmodelfactslabels AT michaelgao presentingmachinelearningmodelinformationtoclinicalenduserswithmodelfactslabels AT nathanbrajer presentingmachinelearningmodelinformationtoclinicalenduserswithmodelfactslabels AT sureshbalu presentingmachinelearningmodelinformationtoclinicalenduserswithmodelfactslabels
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Presenting machine learning model information to clinical end users with model facts labels

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