A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning

• Published in: American journal of bioethics Vol. 22; no. 5; pp. 8 - 22
• Main Authors: McCradden, Melissa D, Anderson, James A, A. Stephenson, Elizabeth, Drysdale, Erik, Erdman, Lauren, Goldenberg, Anna, Zlotnik Shaul, Randi
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 04.05.2022
• Subjects: Artificial Intelligence; Delivery of Health Care; Ethics committees; Ethics Committees, Research; Ethics, Research; health care delivery; human subjects research; Humans; Informed Consent; IRB (Institutional Review Board); Machine Learning; Prospective Studies; research ethics; IRB (Institutional Review Board); health care delivery; informed consent; human subjects research; research ethics; Ethics committees
• ISSN: 1526-5161; 1536-0075; 1536-0075
• DOI: 10.1080/15265161.2021.2013977

The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications.