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CRM: Clinical Reasoning Model

A fine-tuned small language model trained on 511 physician-annotated clinical cases. Walks through differential diagnosis step by step — with fewer hallucinations than the base model.


Why did I do this? Because I initially used the base model and found it was too dumb to understand medical terminology. This made it say stupid stuff. So, I learned how to fine-tune it. This means I just took the already completed model and taught it further using the DiReCT dataset. I used that dataset because it was free and easily accessible. Admittedly, it is a small dataset, as far as fine-tuning goes - but it worked for my purposes. The data below shows the result of this fine-tuning: it understood medical terminology much better, and was able to actually function inside the AUC app like I wanted. Reasonably well at least. It's still only a 3 billion parameter model, so its still suboptimal.


Below is a test case example to show how it improved:

Llama 3.2 3B Instruct LoRA fine-tuning DiReCT dataset Google Colab T4 GPU

Training Overview

511Clinical cases used
3Training epochs
22 → 3.7Loss (start to end)

Test Case

“A 32-year-old woman presents with fatigue, weight gain, cold intolerance, and constipation for 6 months. She also notes her hair has been falling out.”

Model Outputs — Side by Side

Base Llama 3.2 3B (no fine-tuning)

Step 1: The primary complaint is related to the endocrine system. Step 2: Fatigue can be caused by anemia or hypothyroidism... but in most cases it is due to iron deficiency anaemia... Step 3: Weight loss may occur if there was significant reduction in appetite... Step 4: Cold intolerance occurs when your body produces too much thyroxine hormone; you feel hot even after being indoors... Step 5: Constipation could result from low estrogen, medications, poor diet, dehydration, stress and anxiety... Potential diagnosis: HypoThyroidism. Further testing required.

Fine-tuned model (ploppy2/Clinical-Reasoning-Test1)

Step 1: The most likely underlying cause is hypothyroidism. The primary symptom that led to this conclusion was cold intolerance, which suggests decreased thyroid hormone production. Step 2: There may be an autoimmune process involved — Hashimoto's disease is a common cause of hypothyroidism. Step 3: Thyroid function tests will confirm whether she suffers from low TSH levels indicating underactive thyroid gland. Step 4: If confirmed by lab testing, treatment should start immediately. Options include synthetic replacement medication (levothyroxine) daily.

What Changed

Base model problems

  • States cold intolerance = too much thyroid hormone (factually wrong)
  • Claims weight loss occurs (wrong — hypothyroidism causes weight gain)
  • TSH description devolves into unrelated topics (bone health, memory, immunity)
  • Disorganized and clinically unfocused

Fine-tuned model improvements

  • Correctly identifies hypothyroidism from symptom pattern
  • Appropriately mentions Hashimoto's as autoimmune cause
  • Logical flow: symptoms → diagnosis → testing → treatment
  • Stays clinically relevant throughout

Conclusion

Fine-tuning was successful. The model trained on 511 physician-annotated DiReCT cases demonstrably improved diagnostic reasoning structure and reduced clinical hallucinations compared to the base model. This serves as a proof of concept for using fine-tuned small language models in clinical medical education.

Potential Next Steps

  1. Expand training data beyond 511 cases for broader clinical coverage
  2. Upgrade to Llama 3.2 8B with a more powerful GPU for better baseline capability
  3. Clean up training data to reduce disclaimer and hedging language in outputs
  4. Evaluate systematically across a held-out test set of cases
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