Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework. (20th September 2020)

• Record Type: Journal Article
• Authors: Ferryman, Kadija
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 12(2020)
• Page Start: 2016
• Page End: 2019
• Publication Date: 2020-09-20
• Subjects: health disparities -- artificial intelligence -- health policy -- machine learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa133
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. 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.