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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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Detalles Bibliográficos
Autores principales: Sendak, Mark P., Gao, Michael, Brajer, Nathan, Balu, Suresh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090057/
https://www.ncbi.nlm.nih.gov/pubmed/32219182
http://dx.doi.org/10.1038/s41746-020-0253-3
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author Sendak, Mark P.
Gao, Michael
Brajer, Nathan
Balu, Suresh
author_facet Sendak, Mark P.
Gao, Michael
Brajer, Nathan
Balu, Suresh
author_sort Sendak, Mark P.
collection PubMed
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.
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spelling pubmed-70900572020-03-26 Presenting machine learning model information to clinical end users with model facts labels Sendak, Mark P. Gao, Michael Brajer, Nathan Balu, Suresh NPJ Digit Med Comment 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. Nature Publishing Group UK 2020-03-23 /pmc/articles/PMC7090057/ /pubmed/32219182 http://dx.doi.org/10.1038/s41746-020-0253-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Comment
Sendak, Mark P.
Gao, Michael
Brajer, Nathan
Balu, Suresh
Presenting machine learning model information to clinical end users with model facts labels
title 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_short 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
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090057/
https://www.ncbi.nlm.nih.gov/pubmed/32219182
http://dx.doi.org/10.1038/s41746-020-0253-3
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