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

arXiv cs.CL / 4/1/2026

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Key Points

The paper argues that medical LLM evaluations often assume ideal patient questions, but real consultations include unclear or misleading inputs that can undermine safety.
It defines four clinically grounded challenging patient behaviors—information contradiction, factual inaccuracy, self-diagnosis, and care resistance—and provides failure criteria for unsafe model responses.
The authors introduce CPB-Bench, a bilingual (English/Chinese) benchmark of 692 annotated, multi-turn medical dialogues built from four existing datasets.
Across multiple open- and closed-source LLMs, overall performance is strong, but models show consistent, behavior-specific failure patterns, especially with contradictory or medically implausible information.
The study tests four intervention strategies and finds improvements are inconsistent and may sometimes lead to unnecessary corrections; the dataset and code are released.

Abstract

Large language models (LLMs) are increasingly used for medical consultation and health information support. 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. 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 criteria that capture unsafe responses. Building on four existing medical dialogue datasets, we introduce CPB-Bench (Challenging Patient Behaviors Benchmark), a bilingual (English and Chinese) benchmark of 692 multi-turn dialogues annotated with these behaviors. We evaluate a range of open- and closed-source LLMs on their responses to challenging patient utterances. While models perform well overall, we identify consistent, behavior-specific failure patterns, with particular difficulty in handling contradictory or medically implausible patient information. We also study four intervention strategies and find that they yield inconsistent improvements and can introduce unnecessary corrections. We release the dataset and code.