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.

通过结合大型语言模型和现实临床场景，CureFun 提供了一种虚拟模拟患者教育框架，可以促进学生和模拟患者之间的自然对话，评估他们的对话并提供建议，以提升学生的临床查询技能。此外，借助 CureFun 的评估能力，我们还对几种医学大型语言模型进行了评估，并从诊断能力的角度讨论了使用大型语言模型作为虚拟医生的可能性和局限性。