Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

TLDR

Machine learning and AI offer new ways to use growing data for patient benefit, yet the literature lacks transparency, reproducibility, ethical scrutiny, and clear evidence of effectiveness. The authors aim to provide a set of 20 critical questions (TREE) to guide interdisciplinary ML/AI health research toward transparency, reproducibility, ethics, and effectiveness. They propose a preliminary solution by developing these 20 best‑practice questions tailored to ML/AI in health. The questions serve as a framework for researchers, reviewers, and stakeholders to design, evaluate, and appraise ML/AI health studies for patient benefit.

Abstract

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.