Doctor-patient consultations require multi-turn, context-aware communication tailored to diverse patient personas. Training or evaluating doctor LLMs in such settings requires realistic patient interaction systems. However, existing simulators often fail to reflect the full range of personas seen in clinical practice. To address this, we introduce PatientSim, a patient simulator that generates realistic and diverse patient personas for clinical scenarios, grounded in medical expertise. PatientSim operates using: 1) clinical profiles, including symptoms and medical history, derived from real-world data in the MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by four axes: personality, language proficiency, medical history recall level, and cognitive confusion level, resulting in 37 unique combinations. We evaluate eight LLMs for factual accuracy and persona consistency. The top-performing open-source model, Llama 3.3 70B, is validated by four clinicians to confirm the robustness of our framework. As an open-source, customizable platform, PatientSim provides a reproducible and scalable solution that can be customized for specific training needs. Offering a privacy-compliant environment, it serves as a robust testbed for evaluating medical dialogue systems across diverse patient presentations and shows promise as an educational tool for healthcare. The code is available at https://github.com/dek924/PatientSim.

翻译：医患问诊需要针对多样化患者角色进行多轮、上下文感知的沟通。在此类场景中训练或评估医生大语言模型需要真实的患者交互系统。然而，现有模拟器往往无法充分反映临床实践中观察到的完整人物角色谱系。为此，我们提出了PatientSim，一种基于医学专业知识、能为临床场景生成真实且多样化患者角色的患者模拟器。PatientSim通过以下方式运行：1）临床档案，包括从MIMIC-ED和MIMIC-IV真实世界数据中提取的症状与病史；2）由四个维度定义的人物角色：性格特征、语言熟练度、病史回忆水平及认知混淆程度，共形成37种独特组合。我们评估了八种大语言模型的事实准确性与角色一致性。表现最佳的开源模型Llama 3.3 70B经四位临床医生验证，证实了我们框架的稳健性。作为一个开源、可定制平台，PatientSim提供了可复现且可扩展的解决方案，可根据特定训练需求进行定制。该平台提供符合隐私保护要求的环境，可作为评估医疗对话系统在不同患者表现下的稳健测试平台，并展现出作为医疗教育工具的潜力。代码发布于https://github.com/dek924/PatientSim。