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The primary aim of this research was to address the limitations observed inthe medical knowledge of prevalent large language models (LLMs) such asChatGPT, by creating a specialized language model with enhanced accuracy inmedical advice. We achieved this by adapting and refining the large languagemodel meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialoguessourced from a widely used online medical consultation platform. Theseconversations were cleaned and anonymized to respect privacy concerns. Inaddition to the model refinement, we incorporated a self-directed informationretrieval mechanism, allowing the model to access and utilize real-timeinformation from online sources like Wikipedia and data from curated offlinemedical databases. The fine-tuning of the model with real-world patient-doctorinteractions significantly improved the model's ability to understand patientneeds and provide informed advice. By equipping the model with self-directedinformation retrieval from reliable online and offline sources, we observedsubstantial improvements in the accuracy of its responses. Our proposedChatDoctor, represents a significant advancement in medical LLMs, demonstratinga significant improvement in understanding patient inquiries and providingaccurate advice. Given the high stakes and low error tolerance in the medicalfield, such enhancements in providing accurate and reliable information are notonly beneficial but essential.

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge