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

Jenna Wiens, Suchi Saria, Mark Sendak, Marzyeh Ghassemi, Vincent X. Liu, Finale Doshi-Velez, Kenneth Jung, Katherine Heller, David Kale, Mohammed Saeed, Pilar N. Ossorio, Sonoo Thadaney-Israni, Anna Goldenberg

Research output: Contribution to journalArticle

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).

Original languageEnglish (US)
JournalNature medicine
DOIs
StateAccepted/In press - Jan 1 2019

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (Accepted/In press). Do no harm: a roadmap for responsible machine learning for health care. Nature medicine. https://doi.org/10.1038/s41591-019-0548-6