Application of Artificial Intelligence Communication Training Tools in Medical Undergraduate Education: mixed methods feasibility study within a Primary Care Context (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Effective communication is fundamental to high-quality healthcare delivery, influencing patient satisfaction, adherence to treatment plans, and clinical outcomes. However, communication skills training for medical undergraduates often faces challenges in scalability, resource allocation, and personalization. Traditional methods, such as role-playing with standardized patients, are resource-intensive and may not provide consistent feedback tailored to individual learners' needs. Artificial Intelligence (AI) offers realistic patient interactions for education. </sec> <sec> <title>OBJECTIVE</title> This study aims to investigate the application of Artificial Intelligence (AI) -powered communication training tools in medical undergraduate education within a primary care context. The study evaluates the effectiveness, usability, and impact of AI virtual patients (VPs) on medical students' experience in communication skills practice </sec> <sec> <title>METHODS</title> A mixed methods sequential explanatory design was employed, comprising a quantitative survey followed by qualitative focus group discussions. Eighteen participants, including 15 medical students and 3 practicing doctors, engaged with an AI VP simulating a primary care consultation for prostate cancer risk assessment. The AI VP was designed using a large language model (LLM) and natural voice synthesis to create realistic patient interactions. The survey assessed five domains: fidelity, immersion, intrinsic motivation, debrief, and system usability. Focus groups explored participants' experiences, challenges, and perceived educational value of the AI tool. </sec> <sec> <title>RESULTS</title> A mixed methods sequential explanatory design was employed, comprising a quantitative survey followed by qualitative focus group discussions. Eighteen participants, including 15 medical students and 3 practicing doctors, engaged with an AI VP simulating a primary care consultation for prostate cancer risk assessment. The AI VP was designed using a large language model (LLM) and natural voice synthesis to create realistic patient interactions. The survey assessed five domains: fidelity, immersion, intrinsic motivation, debrief, and system usability. Focus groups explored participants' experiences, challenges, and perceived educational value of the AI tool. </sec> <sec> <title>CONCLUSIONS</title> AI can significantly enhance communication skills training for medical undergraduates by providing a scalable, accessible, and realistic simulation environment. Despite some technical challenges, the AI tool was well-received, indicating its potential for broader adoption in medical education. Continued development and refinement of AI technologies will be essential to prepare future healthcare professionals for real-world patient interactions. </sec>

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