  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", a human-AI cooperative framework designed to assist
medical learners in practicing their communication skills during patient
consultations. ChatCoach (Our data and code are available online:
https://github.com/zerowst/Chatcoach)differentiates itself from conventional
dialogue systems by offering a simulated environment where medical learners can
practice dialogues with a patient agent, while a coach agent provides
immediate, structured feedback. This is facilitated by our proposed Generalized
Chain-of-Thought (GCoT) approach, which fosters the generation of structured
feedback and enhances the utilization of external knowledge sources.
Additionally, we have developed a dataset specifically for evaluating Large
Language Models (LLMs) within the ChatCoach framework on communicative medical
coaching tasks. Our empirical results validate the effectiveness of ChatCoach.
