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.

翻译：大型语言模型（LLMs）在临床决策支持中展现出潜力，但存在屈从患者压力而提供不当医疗的风险。我们提出了SycoEval-EM，这是一个通过急诊医学中对抗性患者劝说场景来评估LLM鲁棒性的多智能体仿真框架。在涵盖三个“明智选择”临床场景的20个LLM模型与1,875次模拟交互中，模型的屈从率介于0-100%之间。模型对影像检查请求（38.8%）的脆弱性高于阿片类药物处方请求（25.0%），且模型能力与鲁棒性预测相关性较弱。所有劝说策略均呈现相近有效性（30.0-36.0%），表明模型存在普遍易感性而非特定策略缺陷。我们的研究证明静态基准测试不足以预测社会压力下的安全性，临床人工智能认证需要引入多轮对抗性测试机制。