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However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.<\/jats:p>","DOI":"10.1093\/jamia\/ocaa133","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T19:10:41Z","timestamp":1591729841000},"page":"2016-2019","source":"Crossref","is-referenced-by-count":53,"title":["Addressing health disparities in the Food and Drug Administration\u2019s artificial intelligence and machine learning regulatory framework"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8552-1822","authenticated-orcid":false,"given":"Kadija","family":"Ferryman","sequence":"first","affiliation":[{"name":"Tandon School of Engineering, New York University, Brooklyn, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,20]]},"reference":[{"issue":"3","key":"2020121009242566200_ocaa133-B1","first-page":"1","article-title":"Centers for Disease Control and Prevention. 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