According to Ethan Mollick, people already ask many medical questions to AI, yet evidence on benefits and harms remains thin; most studies benchmark older models against clinicians rather than the real information patients would get without AI. As reported by the linked paper referenced in Mollick’s post, published research often evaluates outdated LLMs and physician-level accuracy, while underexamining comparisons to typical patient pathways such as top search results, health forums, or insurer portals. According to this critique, business value now hinges on measuring new models like GPT4 class, Claude3, and Med-PaLM 2 against non-AI baselines for accuracy, safety, readability, and actionability. For healthcare providers, payers, and digital health startups, the opportunity lies in A/B testing LLM guidance against incumbent channels, auditing hallucination and clinical safety with FDA-aligned frameworks, and quantifying outcomes such as reduced call-center load and improved guideline adherence. As reported by Mollick’s discussion, investors should prioritize studies that use current models, patient-relevant tasks, and outcome metrics that reflect what users would otherwise encounter without AI.