DISC-MedLLM: Bridging General Large Language Models and Real-World
  Medical Consultation
Zhijie Bao Wei Chen Shengze Xiao Kuang Ren Jiaao Wu Cheng Zhong Jiajie Peng Xuanjing Huang Zhongyu Wei
We propose DISC-MedLLM, a comprehensive solution that leverages Large
Language Models (LLMs) to provide accurate and truthful medical response in
end-to-end conversational healthcare services. To construct high-quality
Supervised Fine-Tuning (SFT) datasets, we employ three strategies: utilizing
medical knowledge-graphs, reconstructing real-world dialogues, and
incorporating human-guided preference rephrasing. These datasets are
instrumental in training DISC-MedLLM, surpassing existing medical LLMs in both
single-turn and multi-turn consultation scenarios. Extensive experimental
results demonstrate the effectiveness of the proposed model in bridging the gap
between general language models and real-world medical consultation.
Additionally, we release the constructed dataset and model weights to further
contribute to research and development. Further details and resources can be
found at https://github.com/FudanDISC/DISC-MedLLM