Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations
Large language models (LLMs) are increasingly used for medical consultation and health information support, where safety depends not only on medical knowledge but also on robust responses to unclear, inconsistent, or misleading patient input. However, most existing medical LLM evaluations assume idealized and well-posed patient questions, limiting their realism. We study challenging patient behaviors that commonly arise in real medical consultations and complicate safe clinical reasoning. We define four clinically grounded categories of such behaviors: information contradiction, factual inaccuracy, self-diagnosis, and care resistance. For each behavior, we specify concrete failure criteri...
| Authors | Yahan Li, Xinyi Jie, Wanjia Ruan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 1 October 2026 |
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