Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

Ariel Dora Stern; W Nicholson Price, II

Article | Biostatistics | Forthcoming

Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

by Ariel Dora Stern and W Nicholson Price, II

Abstract

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.

Keywords: Machine learning; causal inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms;

Format: Print

Find at Harvard Read Now

Citation:

Stern, Ariel Dora, and W Nicholson Price, II. "Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning." Biostatistics (forthcoming). (Pre-published online November 19, 2019.)

The development of intelligent machines holds great promise for making health care delivery more accurate, efficient, and accessible, but challenges remain for incorporating AI into clinical and administrative settings.

This case provides an update to the (A) case, which introduces students to adaptive platform trials, an ambitious, more efficient type of clinical trial that increases access to therapies. The A case centers on Dr. Brian Alexander’s efforts to launch an adaptive platform trial for studying potential therapies for glioblastoma (GBM), an aggressive form of brain cancer. The (B) case provides an update and explores the strategies that Alexander is considering for sustainably financing this trial.

The use of biomarkers holds great promise for the development of new therapeutics and the acceleration of clinical research. However, biomarkers must be validated—a complex and costly endeavor. Importantly, biomarker validation is meaningfully shaped by economic and policy-driven incentives.

Faculty & Research

Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

Abstract

Citation:

About the Author

More from the Author