Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations

ArXi:2603.29373v1 Announce Type: new Large language models (LLMs) are increasingly used for medical consultation and health information. In this high-stakes setting, safety depends not only on medical knowledge, but also on how models respond when patient inputs are unclear, inconsistent, or misleading. However, most existing medical LLM evaluations assume idealized and well-posed patient questions, which limits their realism. In this paper, we study challenging patient behaviors that commonly arise in real medical consultations and complicate safe clinical reasoning.