The detailed clinical records drafted by doctors after each patient's visit are crucial for medical practitioners and researchers. Automating the creation of these notes with language models can reduce the workload of doctors. However, training such models can be difficult due to the limited public availability of conversations between patients and doctors. In this paper, we introduce NoteChat, a cooperative multi-agent framework leveraging Large Language Models (LLMs) for generating synthetic doctor-patient conversations conditioned on clinical notes. NoteChat consists of Planning, Roleplay, and Polish modules. We provide a comprehensive automatic and human evaluation of NoteChat, comparing it with state-of-the-art models, including OpenAI's ChatGPT and GPT-4. Results demonstrate that NoteChat facilitates high-quality synthetic doctor-patient conversations, underscoring the untapped potential of LLMs in healthcare. This work represents the first instance of multiple LLMs cooperating to complete a doctor-patient conversation conditioned on clinical notes, offering promising avenues for the intersection of AI and healthcare

翻译：医生在每次患者就诊后撰写的详细临床记录对医疗从业者和研究人员至关重要。利用语言模型自动生成这些笔记可减轻医生的工作负担。然而，由于患者与医生之间的对话数据公开获取有限，训练此类模型存在困难。本文提出NoteChat——一种基于大型语言模型的协作式多智能体框架，用于生成基于临床笔记的合成医患对话。NoteChat包含规划、角色扮演和润色三个模块。我们通过全面的自动评估和人工评估，将NoteChat与包括OpenAI的ChatGPT和GPT-4在内的最先进模型进行对比。结果表明，NoteChat能生成高质量的合成医患对话，凸显了大型语言模型在医疗领域尚未被充分开发的潜力。本工作首次实现多个大型语言模型协作完成基于临床笔记的医患对话，为人工智能与医疗领域的交叉研究提供了有前景的方向。