Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations
This study tested how well large language models handle difficult patient behaviors like contradictions or self-diagnosis during medical consultations. Using a new benchmark, researchers found that models often fail at the first response by ignoring inconsistencies or accepting false claims without correction. The results show that while prompting helps, some methods increase errors, highlighting a need for better interactional safety testing.