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Senayy
Senayy

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Building a Clinical AI Assistant with RAG and GPT-4

#ai #python #healthcare #webdev

What I Built

I recently built Heidihack - an AI-powered clinical decision support system that helps healthcare professionals with:

  • 📋 Automated SOAP Notes - Generates complete clinical documentation
  • 🔍 Differential Diagnoses - AI-suggested diagnoses with risk levels
  • 💊 ICD-10 Coding - Automatic medical billing codes
  • ✅ Treatment Plans - Evidence-based recommendations
  • ⚠️ Safety Checks - Verifies medication allergies

The Tech Stack

Backend:

  • Python + FastAPI
  • OpenAI GPT-4
  • RAG (Retrieval-Augmented Generation)
  • FAISS for vector search

Frontend:

  • React 18
  • Vite
  • Tailwind CSS

How RAG Works

Instead of just asking GPT-4 directly, my system:

  1. Takes patient symptoms and data
  2. Searches a clinical knowledge base for similar cases (using FAISS vector search)
  3. Retrieves relevant medical patterns
  4. Sends both the query AND retrieved context to GPT-4
  5. Generates accurate, grounded clinical analysis

This reduces hallucinations and provides more reliable medical recommendations.

Key Code: The RAG Pipeline 

class ClinicalRAG:
    def generate_analysis(self, form_data, patient_context):
        # Build search query from patient data
        query = self._build_query(form_data, patient_context)

        # Retrieve similar clinical cases
        similar_cases = self.retrieve(query, top_k=10)

        # Generate analysis with GPT-4 + context
        response = self._generate_with_gpt4(
            query=query,
            context=similar_cases,
            patient=patient_context
        )

        return response
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What I Learned

1. RAG > Fine-tuning for Medical AI
For healthcare knowledge that changes frequently, RAG is more practical. You can update the knowledge base without retraining.

2. Prompt Engineering is Critical
Spent 80% of time perfecting prompts to get structured, safe medical outputs.

3. Safety First in Healthcare AI
Added explicit allergy checking and contraindication warnings in the prompts.

4. Caching Saves Time
Caching embeddings reduced startup from 30 seconds to instant.

Demo & Source Code

⭐ GitHub: Heidihack

The repo includes:

  • Complete RAG implementation
  • Clinical knowledge base examples
  • Frontend React app
  • API documentation

Future Plans

  • Real EHR integration (FHIR/HL7)
  • Evidence citations from medical literature
  • Multi-language support
  • Mobile app version

Let's Connect!

Building healthcare AI is fascinating! I'd love to hear your thoughts or answer questions.

Have you worked with RAG systems? Drop a comment below! 👇

If you found this helpful, give the GitHub repo a star! ⭐

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