Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qualified SPs, along with the heavy workload and potential risks they face in consistently portraying actual patients, limit students' access to this type of clinical training. Consequently, the integration of computer program-based simulated patients has emerged as a valuable educational tool in recent years. With the rapid development of Large Language Models (LLMs), their exceptional capabilities in conversational artificial intelligence and role-playing have been demonstrated, making them a feasible option for implementing Virtual Simulated Patient (VSP). In this paper, we present an integrated model-agnostic framework called CureFun that harnesses the potential of LLMs in clinical medical education. This framework facilitates natural conversations between students and simulated patients, evaluates their dialogue, and provides suggestions to enhance students' clinical inquiry skills. Through comprehensive evaluations, our approach demonstrates more authentic and professional SP-scenario dialogue flows compared to other LLM-based chatbots, thus proving its proficiency in simulating patients. Additionally, leveraging CureFun's evaluation ability, we assess several medical LLMs and discuss the possibilities and limitations of using LLMs as virtual doctors from the perspective of their diagnostic abilities.

翻译：模拟病患（SP）在临床医学教育中扮演着关键角色，通过提供逼真的场景供学生练习。然而，培训与雇佣合格SP的高昂成本，以及他们在持续模拟真实病患时面临的工作负荷与潜在风险，限制了学生接受此类临床训练的机会。因此，近年来，基于计算机程序的模拟病患已成为一种有价值的教育工具。随着大型语言模型（LLM）的快速发展，其在对话式人工智能与角色扮演方面的卓越能力已被证实，使其成为实现虚拟模拟病患（VSP）的可行选择。本文提出了一个名为CureFun的模型无关集成框架，该框架充分利用了LLM在临床医学教育中的潜力。该框架能够促进学生与模拟病患之间的自然对话，评估他们的对话内容，并提供建议以提升学生的临床问诊技能。通过全面评估，与其他基于LLM的聊天机器人相比，我们的方法生成了更真实、更专业的SP场景对话流，从而证明了其在模拟病患方面的有效性。此外，借助CureFun的评估能力，我们评估了多个医学LLM，并从诊断能力的角度探讨了使用LLM作为虚拟医生的可能性与局限性。