Title: MedRedFlag: Examining the Tendency of LLMs to Propagate Misconceptions in Practical Health Dialogues

Abstract:

Patients frequently pose health-related inquiries that inadvertently contain incorrect assumptions or false premises. In clinical settings, safe communication protocols dictate that providers must first correct these implicit errors before addressing the patient’s actual concerns, rather than answering the flawed question as stated. Despite the growing reliance of laypeople on large language models (LLMs) for medical guidance, this specific competency has not been adequately evaluated. Consequently, this study explores how LLMs handle false premises within authentic health queries. To facilitate this investigation, we created a semi-automated workflow to assemble MedRedFlag, a collection of over 1,100 Reddit-sourced questions that necessitate redirection. We then conduct a systematic comparison of responses generated by leading LLMs against those provided by medical professionals. Our findings indicate that LLMs frequently neglect to redirect problematic inquiries, even when they successfully identify the erroneous premise, thereby delivering answers that may result in suboptimal healthcare decisions. This benchmark underscores a significant and previously unrecognized deficiency in LLM performance within real-world health communication contexts, raising serious safety issues for AI systems intended for patient use. The associated code and dataset can be accessed at https://github.com/srsambara-1/MedRedFlag.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC