Borg Alexander, Georg Carina, Jobs Benjamin, Huss Viking, Waldenlind Kristin, Ruiz Mini, Edelbring Samuel, Skantze Gabriel, Parodis Ioannis
Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, and Center for Molecular Medicine (CMM), Stockholm, Sweden.
Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
J Med Internet Res. 2025 Mar 3;27:e63312. doi: 10.2196/63312.
Virtual patients (VPs) are computer-based simulations of clinical scenarios used in health professions education to address various learning outcomes, including clinical reasoning (CR). CR is a crucial skill for health care practitioners, and its inadequacy can compromise patient safety. Recent advancements in large language models (LLMs) and social robots have introduced new possibilities for enhancing VP interactivity and realism. However, their application in VP simulations has been limited, and no studies have investigated the effectiveness of combining LLMs with social robots for CR training.
The aim of the study is to explore the potential added value of a social robotic VP platform combined with an LLM compared to a conventional computer-based VP modality for CR training of medical students.
A Swedish explorative proof-of-concept study was conducted between May and July 2023, combining quantitative and qualitative methodology. In total, 15 medical students from Karolinska Institutet and an international exchange program completed a VP case in a social robotic platform and a computer-based semilinear platform. Students' self-perceived VP experience focusing on CR training was assessed using a previously developed index, and paired 2-tailed t test was used to compare mean scores (scales from 1 to 5) between the platforms. Moreover, in-depth interviews were conducted with 8 medical students.
The social robotic platform was perceived as more authentic (mean 4.5, SD 0.7 vs mean 3.9, SD 0.5; odds ratio [OR] 2.9, 95% CI 0.0-1.0; P=.04) and provided a beneficial overall learning effect (mean 4.4, SD 0.6 versus mean 4.1, SD 0.6; OR 3.7, 95% CI 0.1-0.5; P=.01) compared with the computer-based platform. Qualitative analysis revealed 4 themes, wherein students experienced the social robot as superior to the computer-based platform in training CR, communication, and emotional skills. Limitations related to technical and user-related aspects were identified, and suggestions for improvements included enhanced facial expressions and VP cases simulating multiple personalities.
A social robotic platform enhanced by an LLM may provide an authentic and engaging learning experience for medical students in the context of VP simulations for training CR. Beyond its limitations, several aspects of potential improvement were identified for the social robotic platform, lending promise for this technology as a means toward the attainment of learning outcomes within medical education curricula.
虚拟患者（VPs）是基于计算机的临床场景模拟，用于卫生专业教育，以实现各种学习成果，包括临床推理（CR）。临床推理是医疗从业者的一项关键技能，其不足可能会危及患者安全。大语言模型（LLMs）和社交机器人的最新进展为增强虚拟患者的交互性和真实感带来了新的可能性。然而，它们在虚拟患者模拟中的应用一直有限，且尚无研究调查将大语言模型与社交机器人相结合用于临床推理训练的有效性。
本研究的目的是探索与传统的基于计算机的虚拟患者模式相比，社交机器人虚拟患者平台与大语言模型相结合在医学生临床推理训练方面的潜在附加价值。
2023年5月至7月进行了一项瑞典探索性概念验证研究，结合了定量和定性方法。来自卡罗林斯卡学院和一个国际交流项目的15名医学生在一个社交机器人平台和一个基于计算机的半线性平台上完成了一个虚拟患者案例。使用先前开发的指标评估学生对专注于临床推理训练的虚拟患者体验的自我感知，并使用配对双尾t检验比较两个平台之间的平均得分（1至5分）。此外，对8名医学生进行了深入访谈。
与基于计算机的平台相比，社交机器人平台被认为更真实（平均分4.5，标准差0.7对平均分3.9，标准差0.5；优势比[OR]2.9，95%置信区间0.0 - 1.0；P = 0.04），并提供了有益的整体学习效果（平均分4.4，标准差0.6对平均分4.1，标准差0.6；OR 3.7，95%置信区间0.1 - 0.5；P = 0.01）。定性分析揭示了4个主题，其中学生体验到社交机器人在临床推理、沟通和情感技能训练方面优于基于计算机的平台。确定了与技术和用户相关方面相关的局限性，改进建议包括增强面部表情和模拟多种人格的虚拟患者案例。
在用于临床推理训练的虚拟患者模拟背景下，由大语言模型增强的社交机器人平台可能为医学生提供真实且引人入胜的学习体验。除了其局限性外，还确定了社交机器人平台几个潜在改进方面，这为该技术有望成为实现医学教育课程学习成果的一种手段带来了希望。
