Integrating large language model-based agents into a virtual patient chatbot for clinical anamnesis training

Abstract

Effective communication is crucial for trust-building, accurate information gathering, and clinical decision-making in healthcare. Despite its emphasis in medical curricula, traditional training methods, such as role-playing with standardized patients, remain costly, logistically complex, and fail to replicate real-life scenarios. Simulation-based training enhances communication and reasoning skills, but novice learners often struggle due to underdeveloped reasoning processes. Furthermore, limited access to asynchronous, autonomous simulated patient interactions restricts personalized practice. Virtual patient models offer scalable solutions with interactive scenarios and tailored feedback, but high development costs and resource demands hinder their widespread adoption. To address these challenges, virtual patient systems powered by Large Language Models (LLMs) have emerged as a promising tool. These generative agents simulate human-like behavioral responses by leveraging LLM capabilities, cognitive mechanisms, and contextual memory retrieval. A tool was developed allowing students to select clinical cases and interact with a chatbot simulating a patient role. Teachers can also create custom cases. Evaluations showed that the agent provided consistent, plausible responses aligned with case descriptions and achieved a Chatbot Usability Questionnaire (CUQ) score of 86.25/100. Our results show that this approach enables flexible, repetitive, and asynchronous practice while offering real-time feedback.

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