Making clinicians worthy of medical AI: Lessons from Tesla - STAT

Tesla is in the midst of conducting an unprecedented social experiment: testing drivers of its cars to see if they are safe enough operators to receive the company's Full Self-Driving (FSD) Beta software update, which expands the car's autonomous capabilities, most notably on city streets. The company is automatically evaluating humans based on a safety score composed of five factors, including forward collision warnings per thousand miles driven, aggressive turning, and forced autopilot disengagements. While the societal conversation around artificial intelligence tends to focus on machine abilities, Tesla's experiment turns the spotlight onto the human: Is the driver responsible enough to be given the superpower? As medical researchers, we realized this question may be at the heart of an exciting paradigm for making AI-assisted medicine a success, though it also poses additional questions: Are safety scores accurate and fair? Will human improvements be durable after the evaluation period once the incentive is earned? After all, interventions evaluated in the pristine setting of clinical studies often underwhelm when deployed in the real world, as shown in studies of drug adherence or weight loss maintenance.