SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

• URL: http://arxiv.org/abs/2601.16529v1
• Date: Fri, 23 Jan 2026 08:01:39 GMT
• Title: SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care
• Abstract summary: Large language models (LLMs) show promise in clinical decision support yet risk acquiescing to patient pressure for inappropriate care.<n>We introduce SycoEval-EM, a multi-agent simulation framework evaluating robustness through adversarial patient persuasion.
• Score: 2.2245087144264657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) show promise in clinical decision support yet risk acquiescing to patient pressure for inappropriate care. We introduce SycoEval-EM, a multi-agent simulation framework evaluating LLM robustness through adversarial patient persuasion in emergency medicine. Across 20 LLMs and 1,875 encounters spanning three Choosing Wisely scenarios, acquiescence rates ranged from 0-100\%. Models showed higher vulnerability to imaging requests (38.8\%) than opioid prescriptions (25.0\%), with model capability poorly predicting robustness. All persuasion tactics proved equally effective (30.0-36.0\%), indicating general susceptibility rather than tactic-specific weakness. Our findings demonstrate that static benchmarks inadequately predict safety under social pressure, necessitating multi-turn adversarial testing for clinical AI certification.

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