Work

Addressing health disparities in the Food and Drug Administration’s artificial intelligence and machine learning regulatory framework

Year: 2020

Type: article

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.

Source: Journal of the American Medical Informatics Association

DOI: 10.1093/jamia/ocaa133

Author: Kadija Ferryman

Institution: New York University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: bronze

OpenAlex ID: W3087068758