Real-world health questions from patients often unintentionally embed false assumptions or premises. In such cases, safe medical communication typically involves redirection: addressing the implicit misconception and then responding to the underlying patient context, rather than the original question. While large language models (LLMs) are increasingly being used by lay users for medical advice, they have not yet been tested for this crucial competency. Therefore, in this work, we investigate how LLMs react to false premises embedded within real-world health questions. We develop a semi-automated pipeline to curate MedRedFlag, a dataset of 1100+ questions sourced from Reddit that require redirection. We then systematically compare responses from state-of-the-art LLMs to those from clinicians. Our analysis reveals that LLMs often fail to redirect problematic questions, even when the problematic premise is detected, and provide answers that could lead to suboptimal medical decision making. Our benchmark and results reveal a novel and substantial gap in how LLMs perform under the conditions of real-world health communication, highlighting critical safety concerns for patient-facing medical AI systems. Code and dataset are available at https://github.com/srsambara-1/MedRedFlag.

翻译：来自患者的真实健康问题常无意中嵌入错误假设或前提。在此类情况下，安全的医疗沟通通常需要引导纠正：先指出隐含的误解，再回应患者所处的实际背景，而非原始问题。尽管非专业用户越来越多地使用大语言模型获取医疗建议，但这些模型尚未针对这一关键能力接受测试。因此，本研究系统探究了大语言模型如何应对真实健康问题中嵌入的错误前提。我们开发了一套半自动化流程，构建了MedRedFlag数据集——包含1100余个来自Reddit且需引导纠正的问题。通过系统对比前沿大语言模型与临床医生的回应，我们发现：即使检测到问题前提有误，大语言模型也常无法正确引导纠正，其应答可能导致次优医疗决策。我们的基准测试与结果揭示了在真实健康沟通场景下大语言模型表现中的新型重大缺陷，凸显了面向患者的医疗AI系统存在的关键安全性问题。代码与数据集见https://github.com/srsambara-1/MedRedFlag。