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ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge

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what chatdoctor can do

AI: ChatDoctor is a language model that has been fine-tuned using doctor-patient conversations related to more than 700 diseases and their symptoms, medical tests, and recommended medications. It has great potential to understand patients' needs, provide informed advice and assistance in medical-related fields. The integration of this advanced language model into healthcare can revolutionize the way healthcare professionals and patients communicate, ultimately improving the efficiency and quality of patient care and outcomes. The source codes, datasets, and model weights of this project are available at https://github.com/Kent0n-Li/ChatDoctor.
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How good is the result? How bad is the hallucination?

AI: The text does not provide information on the "hallucination" or any negative aspects of the resulting models. It states that the fine-tuned LLMs show great potential to understand patients' needs, provide informed advice, and offer valuable assistance in various medical-related fields, ultimately improving the overall efficiency and quality of patient care and outcomes.

Authors

Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, Zhang You

Abstract

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 tailored to the medical domain, resulting in poor answer accuracy and inability to give plausible recommendations for medical diagnosis, medications, etc. To address this issue, we collected more than 700 diseases and their corresponding symptoms, required medical tests, and recommended medications, from which we generated 5K doctor-patient conversations. By fine-tuning LLMs using these tailored doctor-patient conversations, the resulting 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 efficiency and quality of patient care and outcomes. In addition, we made public all the source codes, datasets, and model weights to facilitate the further development of dialogue models in the medical field. The training data, codes, and weights of this project are available at: https://github.com/Kent0n-Li/ChatDoctor.

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