A new stress-testing framework for medical large language models (LLMs) reveals that traditional accuracy benchmarks may overlook critical safety issues. The study indicates that narrative stress auditing is essential for evaluating LLMs in clinical settings, as some models displayed concerning performance under realistic conditions despite high baseline accuracy.