Regulation of predictive analytics in medicine

Algorithms must meet regulatory standards of clinical benefit Artificial intelligence (AI) and increased computing power have long held the promise of improving prediction and prognostication in health care ( 1 ). Now, use of predictive analytics and AI in medicine, though with fits and starts, is transitioning from hype to reality: Several commercial algorithms have received regulatory approval for broad clinical use. But the barrier for entry of new advanced algorithms has been low. To unlock the potential of advanced analytics while protecting patient safety, regulatory and professional bodies should ensure that advanced algorithms meet accepted standards of clinical benefit, just as they do for clinical therapeutics and predictive biomarkers. External validation and prospective testing of advanced algorithms are clearly needed ( 2 ), but recent regulatory clearances raise concerns over the rigor of this process. Given these concerns, we propose five standards to guide regulation of devices based on predictive analytics and AI. Although well-established research standards, such as the TRIPOD Checklist, exist for developing and validating multivariable prediction models in medicine ( 3 ), our standards provide regulatory guidance for such algorithms prior to implementation in clinical settings.