Do no harm: a roadmap for responsible machine learning for health care

Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successf...

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Published inNature medicine Vol. 25; no. 9; pp. 1337 - 1340
Main Authors Wiens, Jenna, Saria, Suchi, Sendak, Mark, Ghassemi, Marzyeh, Liu, Vincent X, Doshi-Velez, Finale, Jung, Kenneth, Heller, Katherine, Kale, David, Saeed, Mohammed, Ossorio, Pilar N, Thadaney-Israni, Sonoo, Goldenberg, Anna
Format Journal Article
LanguageEnglish
Published United States Nature Publishing Group 01.09.2019
Subjects
Algorithms
Bias
Clinical Decision-Making - ethics
Computer engineering
Computer science
Delivery of Health Care - ethics
Delivery of Health Care - trends
Health care
Humans
Learning algorithms
Machine learning
Machine Learning - ethics
Machine Learning - trends
Medicine
Mortality
Translation
United States
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Summary:Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
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ObjectType-Review-1
ISSN:1078-8956
1546-170X
DOI:10.1038/s41591-019-0548-6