%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 }