Model Card for Med-o1-1.7B
Model Details
Med-o1-1.7B is fine-tuned specifically for medical diagnostic reasoning. Using the CoT_Medical_Diagnosis dataset, the model has learned to not only provide medical diagnoses but also to explain the step-by-step clinical reasoning that leads to its conclusions.
Key features of Med-o1-1.7B include:
- Chain-of-Thought (CoT) reasoning: Generates transparent and structured reasoning for diagnostic decisions.
- Clinical logic and evidence synthesis: Mimics human-style differential diagnosis and evaluates patient information systematically.
- Medical domain specialization: Focused entirely on clinical scenarios, from symptom analysis to medical history interpretation.
- Trust and explainability: Designed to build confidence in AI-driven medical assistance by clearly showing how conclusions are reached.
This model is ideal for researchers, educators, and developers aiming to study, demonstrate, or integrate AI-assisted medical reasoning.
Uses
Intended Use
- Educational purposes: Teaching clinical reasoning and differential diagnosis.
- Research applications: Exploring AI in medical decision support and diagnostic logic.
- Prototyping healthcare AI tools: Generating interpretable diagnostic reasoning.
⚠️ Important: This model is not intended for actual medical diagnosis or treatment decisions. Outputs should not be relied upon as a substitute for professional medical judgment. Always consult licensed healthcare professionals.
Bias, Risks, and Limitations
- Not a substitute for professional medical advice or diagnosis
- Trained on a limited dataset (3000+ cases); performance may vary with novel or complex clinical scenarios
Training Data
The model was fine-tuned on the
moremilk/CoT_Medical_Diagnosis dataset:
- Over 3007 detailed medical scenarios
- Each entry includes: patient symptoms, history, reasoning steps (CoT), and final diagnosis
- Scenarios cover a wide range of clinical cases, ensuring broad exposure to medical reasoning patterns
The dataset emphasizes transparent reasoning, helping the model learn to articulate logical steps for arriving at conclusions.
