Regulatory oversight, causal inference, and safe and effective health care machine learning. (19th November 2019)

• Record Type: Journal Article
• Authors: Stern, Ariel Dora; Price, W Nicholson
• Is Part Of: Biostatistics. Volume 21:Number 2(2020)
• Page Start: 363
• Page End: 367
• Publication Date: 2019-11-19
• Subjects: Causal inference -- Device regulation -- FDA -- Machine learning -- Software as a medical device; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxz044
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging actively in thinking about how best to facilitate safe and effective use. Although the scope of its oversight for software-driven products is limited, if FDA takes the lead in promoting and facilitating appropriate applications of causal inference as a part of ML development, that leadership is likely to have implications well beyond regulated products.