Do no harm: a roadmap for responsible machine learning for health care.
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ID: 32843
2019
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Abstract
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).
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wiens2019donature
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| 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; |
| Journal | Nature Medicine |
| Year | 2019 |
| DOI |
10.1038/s41591-019-0548-6
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| URL | |
| Keywords | Keywords not found |
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