利用大语言模型作为临床教育中的模拟病人
Leveraging Large Language Model as Simulated Patients for Clinical Education
AI总结：
针对传统模拟病人成本高、风险大等问题，本文提出基于大语言模型的CureFun框架，通过自然对话、评估和建议提升学生临床问诊技能，并验证了其在模拟病人场景中的优越表现及在评估医疗LLM诊断能力方面的潜力。
摘要：
模拟病人（SPs）在临床医学教育中发挥着至关重要的作用，通过提供真实的场景供学生练习。然而，培训和雇佣合格SPs的高昂成本，加上他们在持续扮演真实病人时所面临的繁重工作量和潜在风险，限制了学生获得此类临床培训的机会。因此，近年来基于计算机程序的模拟病人的整合已成为一种有价值的教育工具。随着大语言模型（LLMs）的快速发展，其在对话式人工智能和角色扮演方面的卓越能力已被证实，这使得它们成为实现虚拟模拟病人（VSP）的可行选择。在本文中，我们提出了一个与模型无关的集成框架，名为CureFun，该框架利用了LLMs在临床医学教育中的发挥潜力。该框架促进了学生与模拟病人之间的自然对话，评估他们的对话，并提供建议以提升学生的临床问诊技能 通过全面的评估，我们的方法展示了真实且专业的SP场景对话流，并优于其他基于LLM的聊天机器人，从而证明了其在模拟病人方面的熟练度。此外，利用CureFun的评估能力，我们评估了几个医疗LLM，并讨论了从诊断能力的角度使用LLMs作为虚拟医生的潜力和局限性。summary_cn
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
