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Effective clinician–patient communication is critical to quality care but is often hindered by medical jargon, cultural barriers, and time constraints. This paper introduces an AI-driven training platform that uses large language models (LLMs) to help clinicians enhance clarity, empathy, and cultural sensitivity. The system provides two complementary modes: a structured dialogue model for novices, offering predefined scenarios and guided practice, and an open-ended model for experienced users, supporting natural, unscripted conversations. In both modes, the AI role-plays as a patient and generates formative feedback on clarity, empathy, and cultural appropriateness. Unlike prior AI tools, which primarily target patient self-service, this system directly addresses the training needs of clinicians. A feasibility study with 17 healthcare professionals from New Zealand and Uganda demonstrated positive reception, with clinicians reporting improved awareness of communication clarity and strong preference for the open-ended model. These findings suggest that AI-powered simulation can serve as a scalable and adaptive framework for communication training in modern healthcare.

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LLM-Based Simulation Tool for Clinician-Patient Communication Training: A Dual-Mode AI Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Magezi Julius,
  • Junhong Zhao,
  • Xiaoying Gao,
  • Jon Herries,
  • Melita MacDonald,
  • Brad Peckler

摘要

Effective clinician–patient communication is critical to quality care but is often hindered by medical jargon, cultural barriers, and time constraints. This paper introduces an AI-driven training platform that uses large language models (LLMs) to help clinicians enhance clarity, empathy, and cultural sensitivity. The system provides two complementary modes: a structured dialogue model for novices, offering predefined scenarios and guided practice, and an open-ended model for experienced users, supporting natural, unscripted conversations. In both modes, the AI role-plays as a patient and generates formative feedback on clarity, empathy, and cultural appropriateness. Unlike prior AI tools, which primarily target patient self-service, this system directly addresses the training needs of clinicians. A feasibility study with 17 healthcare professionals from New Zealand and Uganda demonstrated positive reception, with clinicians reporting improved awareness of communication clarity and strong preference for the open-ended model. These findings suggest that AI-powered simulation can serve as a scalable and adaptive framework for communication training in modern healthcare.