Physicians’ and Machine Learning Researchers’ Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study (Preprint)

Abstract

UNSTRUCTURED Innovative tools leveraging machine learning and artificial intelligence (ML/AI) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One challenge for successful innovation is the absence, to date, of a robust ethical framework informed by the perspectives of ML/AI researchers and physicians. To help articulate these perspectives, we conducted 21 semi-structured interviews with a purposive sample of ML/AI researchers (n = 10) and physicians (n = 11). We asked interviewees about their views regarding ethical considerations related to the adoption of ML/AI in medicine. Notably, both researchers and physicians described concerns regarding how ML/AI innovations are shaped in early phases even prior to their development and implementation (which from here on will be referred to as the “problem formulation” phase). Considerations encompassed assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. These qualitative findings help to elucidate several ethical challenges anticipated or encountered in ML/AI for health care.