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 yet to be adapted for the medical domain, resulting in poor accuracy of responses and an inability to provide sound advice on medical diagnoses, medications, etc. To address this problem, we fine-tuned our ChatDoctor model based on 100k real-world patient-physician conversations from an online medical consultation site. Besides, we add autonomous knowledge retrieval capabilities to our ChatDoctor, for example, Wikipedia or a disease database as a knowledge brain. By fine-tuning the LLMs using these 100k patient-physician conversations, our model showed significant improvements in understanding patients' needs and providing informed advice. The autonomous ChatDoctor model based on Wikipedia and Database Brain can access real-time and authoritative information and answer patient questions based on this information, significantly improving the accuracy of the model's responses, which shows extraordinary potential for the medical field with a low tolerance for error. To facilitate the further development of dialogue models in the medical field, we make available all source code, datasets, and model weights 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，此类语言模型尚未针对医学领域进行适配，导致其响应准确性低下，无法就医学诊断、用药等提供合理建议。为解决这一问题，我们基于来自在线医疗咨询平台的10万条真实医患对话对ChatDoctor模型进行了微调。此外，我们为ChatDoctor增加了自主知识检索能力，例如将维基百科或疾病数据库作为知识大脑。通过使用这10万条医患对话对大型语言模型进行微调，我们的模型在理解患者需求和提供专业建议方面展现出显著提升。基于维基百科和数据库大脑的自主ChatDoctor模型能够获取实时权威信息，并据此回答患者问题，显著提高了模型响应的准确性，这对容错率极低的医学领域展现出非凡潜力。为促进医疗领域对话模型的进一步发展，我们在https://github.com/Kent0n-Li/ChatDoctor 开源了所有源代码、数据集和模型权重。