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).
Description
Do no harm: a roadmap for responsible machine learning for health care | Nature Medicine
%0 Journal Article
%1 wiens2019roadmap
%A Wiens, Jenna
%A Saria, Suchi
%A Sendak, Mark
%A Ghassemi, Marzyeh
%A Liu, Vincent X.
%A Doshi-Velez, Finale
%A Jung, Kenneth
%A Heller, Katherine
%A Kale, David
%A Saeed, Mohammed
%A Ossorio, Pilar N.
%A Thadaney-Israni, Sonoo
%A Goldenberg, Anna
%D 2019
%J Nature Medicine
%K care health intervention learning machine p:2020_reproductive translation translational
%N 9
%P 1337--1340
%R 10.1038/s41591-019-0548-6
%T Do no harm: a roadmap for responsible machine learning for health care
%U https://doi.org/10.1038/s41591-019-0548-6
%V 25
%X 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 = {care health intervention learning machine p:2020_reproductive translation translational},
number = 9,
pages = {1337--1340},
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
}