  The primary aim of this research was to address the limitations observed in
the medical knowledge of prevalent large language models (LLMs) such as
ChatGPT, by creating a specialized language model with enhanced accuracy in
medical advice. We achieved this by adapting and refining the large language
model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues
sourced from a widely used online medical consultation platform. These
conversations were cleaned and anonymized to respect privacy concerns. In
addition to the model refinement, we incorporated a self-directed information
retrieval mechanism, allowing the model to access and utilize real-time
information from online sources like Wikipedia and data from curated offline
medical databases. The fine-tuning of the model with real-world patient-doctor
interactions significantly improved the model's ability to understand patient
needs and provide informed advice. By equipping the model with self-directed
information retrieval from reliable online and offline sources, we observed
substantial improvements in the accuracy of its responses. Our proposed
ChatDoctor, represents a significant advancement in medical LLMs, demonstrating
a significant improvement in understanding patient inquiries and providing
accurate advice. Given the high stakes and low error tolerance in the medical
field, such enhancements in providing accurate and reliable information are not
only beneficial but essential.

Computation and Language