ChatDoctor
| Property | Value |
|---|---|
| Base Model | LLaMA |
| Training Data | InstructorDoctor-200k |
| Paper | arXiv:2303.14070 |
| Authors | Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, Zhang You |
What is ChatDoctor?
ChatDoctor is a specialized medical AI assistant fine-tuned on the LLaMA foundation model using extensive medical domain knowledge. Developed by researchers from multiple institutions including the University of Texas Southwestern Medical Center and the University of Illinois, it aims to provide reliable medical consultations through natural conversation.
Implementation Details
The model is trained on InstructorDoctor-200k, a comprehensive medical dialogue dataset derived from "MedDialog." It uses advanced fine-tuning techniques with FSDP (Fully Sharded Data Parallel) training and supports both BF16 and TF32 precision.
- Fine-tuned using PyTorch with distributed training support
- Implements cosine learning rate scheduling
- Uses gradient accumulation for stable training
- Supports efficient inference through chat interface
Core Capabilities
- Medical dialogue generation and response
- Symptom analysis and preliminary diagnosis suggestions
- Medical test recommendations
- Medication information and advice
- Adaptive learning from patient interactions
Frequently Asked Questions
Q: What makes this model unique?
ChatDoctor specializes in medical conversations by combining LLaMA's general language capabilities with specific medical domain knowledge. It's trained on a large corpus of medical dialogues and can provide contextual medical advice while maintaining a natural conversation flow.
Q: What are the recommended use cases?
The model is designed for preliminary medical consultations, patient education, and general medical information queries. However, it should not replace professional medical advice or emergency care. It's best used as a supplementary tool for medical information and initial symptom assessment.
