Cancer-Myth: Evaluating AI Chatbot on Patient Questions with False Presuppositions

• URL: http://arxiv.org/abs/2504.11373v1
• Date: Tue, 15 Apr 2025 16:37:32 GMT
• Title: Cancer-Myth: Evaluating AI Chatbot on Patient Questions with False Presuppositions
• Authors: Wang Bill Zhu, Tianqi Chen, Ching Ying Lin, Jade Law, Mazen Jizzini, Jorge J. Nieva, Ruishan Liu, Robin Jia,
• Abstract summary: We evaluate large language models (LLMs) on cancer-related questions drawn from real patients.<n>LLMs frequently fail to recognize or address false presuppositions in the questions.<n>These findings expose a critical gap in the clinical reliability of LLMs.
• Score: 16.21971764311474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer patients are increasingly turning to large language models (LLMs) as a new form of internet search for medical information, making it critical to assess how well these models handle complex, personalized questions. However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with detailed clinical contexts. In this paper, we first evaluate LLMs on cancer-related questions drawn from real patients, reviewed by three hematology oncology physicians. While responses are generally accurate, with GPT-4-Turbo scoring 4.13 out of 5, the models frequently fail to recognize or address false presuppositions in the questions-posing risks to safe medical decision-making. To study this limitation systematically, we introduce Cancer-Myth, an expert-verified adversarial dataset of 585 cancer-related questions with false presuppositions. On this benchmark, no frontier LLM -- including GPT-4o, Gemini-1.Pro, and Claude-3.5-Sonnet -- corrects these false presuppositions more than 30% of the time. Even advanced medical agentic methods do not prevent LLMs from ignoring false presuppositions. These findings expose a critical gap in the clinical reliability of LLMs and underscore the need for more robust safeguards in medical AI systems.

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