The evidence challenge facing large language models in medicine

Abstract

The growing use of large language models (LLMs) in medicine presents challenges for traditional approaches to evidence generation and evaluation. Rapid model development and variation in the design and reporting of prospective clinical studies may pose important challenges for timely, meaningful evaluation. This News & Views article explores these challenges and considers emerging strategies for evaluating the efficacy and safety of clinical LLMs.