Recent large language models (LLMs) in the general domain, such as ChatGPT, have shown remarkable success in following instructions and producing human-like responses. However, such language models have not been learned individually and carefully for the medical domain, resulting in poor diagnostic accuracy and inability to give correct recommendations for medical diagnosis, medications, etc. To address this issue, we collected more than 700 diseases and their corresponding symptoms, recommended medications, and required medical tests, and then generated 5K doctor-patient conversations. By fine-tuning models of doctor-patient conversations, these models emerge with great potential to understand patients' needs, provide informed advice, and offer valuable assistance in a variety of medical-related fields. The integration of these advanced language models into healthcare can revolutionize the way healthcare professionals and patients communicate, ultimately improving the overall quality of care and patient outcomes. In addition, we will open all source code, datasets and model weights to advance the further development of dialogue models in the medical field. In addition, the training data, code, and weights of this project are available at: https://github.com/Kent0n-Li/ChatDoctor.

翻译：近期通用领域的大型语言模型（如ChatGPT）在遵循指令和生成类人响应方面取得了显著成功。然而，这类语言模型尚未针对医学领域进行个体化精细学习，导致诊断准确率低下，无法对医疗诊断、用药建议等提供正确指导。为解决该问题，我们收集了700余种疾病及其对应症状、推荐药物和所需医学检查，并生成了5000组医患对话。通过对医患对话模型进行微调，这些模型展现出理解患者需求、提供专业建议以及在多种医疗相关领域提供宝贵协助的巨大潜力。将此类先进语言模型整合至医疗体系，有望彻底改变医护人员与患者的沟通方式，最终提升整体护理质量与患者预后。此外，我们将开源所有源代码、数据集和模型权重，以推动医疗领域对话模型的进一步发展。本项目的训练数据、代码及权重可通过以下链接获取：https://github.com/Kent0n-Li/ChatDoctor