Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems. However, the potential of NLP to benefit inexperienced doctors, particularly in areas such as communicative medical coaching, remains largely unexplored. We introduce ``ChatCoach,'' an integrated human-AI cooperative framework. Within this framework, both a patient agent and a coaching agent collaboratively support medical learners in practicing their medical communication skills during consultations. Unlike traditional dialogue systems, ChatCoach provides a simulated environment where a human doctor can engage in medical dialogue with a patient agent. Simultaneously, a coaching agent provides real-time feedback to the doctor. To construct the ChatCoach system, we developed a dataset and integrated Large Language Models such as ChatGPT and Llama2, aiming to assess their effectiveness in communicative medical coaching tasks. Our comparative analysis demonstrates that instruction-tuned Llama2 significantly outperforms ChatGPT's prompting-based approaches.

翻译：传统自然语言处理（NLP）在医疗领域的应用主要集中于以患者为中心的服务，例如通过医疗对话系统改善患者互动与护理交付。然而，NLP在辅助经验不足的医生（尤其在医学沟通培训等方向）中的潜力仍鲜有探索。我们提出"ChatCoach"——一种人机协同集成框架。在该框架中，患者智能体与教练智能体协同工作，支持医学生在问诊过程中练习医学沟通技巧。与传统对话系统不同，ChatCoach构建了一个模拟环境：人类医生可与患者智能体进行医疗对话，同时教练智能体为医生提供实时反馈。为构建ChatCoach系统，我们开发了一个数据集并集成了ChatGPT与Llama2等大语言模型，旨在评估其在医学沟通培训任务中的有效性。对比分析表明，经过指令微调的Llama2在性能上显著优于基于ChatGPT提示词的方法。