Do no harm: a roadmap for responsible machine learning for health care. 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. Nature Medicine, 25(9):1337–1340, 2019. ![Do no harm: a roadmap for responsible machine learning for health care [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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).
@article{wiens2019roadmap,
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).},
added-at = {2020-07-06T17:07:47.000+0200},
author = {Wiens, Jenna and Saria, Suchi and Sendak, Mark and Ghassemi, Marzyeh and Liu, Vincent X. and Doshi-Velez, Finale and Jung, Kenneth and Heller, Katherine and Kale, David and Saeed, Mohammed and Ossorio, Pilar N. and Thadaney-Israni, Sonoo and Goldenberg, Anna},
biburl = {https://www.bibsonomy.org/bibtex/2ee40c4c733d10db6719211f5200d964f/becker},
description = {Do no harm: a roadmap for responsible machine learning for health care | Nature Medicine},
doi = {10.1038/s41591-019-0548-6},
interhash = {a7398397dde1de95e99f5ccc07dd3215},
intrahash = {ee40c4c733d10db6719211f5200d964f},
issn = {1546170X},
journal = {Nature Medicine},
keywords = {translational p:2020_reproductive machine translation learning intervention health care},
number = 9,
pages = {1337--1340},
privnote = {Overview of the barriers to deployment and translational impact of ML methods for health care according to {davidson2019enabling}.},
refid = {Wiens2019},
timestamp = {2020-07-06T17:36:29.000+0200},
title = {Do no harm: a roadmap for responsible machine learning for health care},
url = {https://doi.org/10.1038/s41591-019-0548-6},
volume = 25,
year = 2019
}
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