#Dr. Amina — AI Healthcare for Northern Nigeria

Northern Nigeria faces a critical healthcare information gap: limited access to doctors, low health literacy, and language barriers that leave millions without reliable medical guidance. Dr. Amina was built to close that gap.

Dr. Amina is a multilingual AI healthcare assistant that answers medical questions using retrieval-augmented generation (RAG) grounded in 648 verified health documents — not hallucinated medical advice. It combines:

• Google Gemini AI for natural language understanding and response generation
• Pinecone vector database for semantic search across the medical knowledge base
• Emergency detection using an LLM-based classifier with an 80% confidence threshold — directs users to the nearest hospital when it matters
• Hausa voice input via speech-to-text, because health literacy starts with language accessibility
• Supabase chat persistence so conversations carry context across sessions

User Query → Gemini Embedding → Pinecone Retrieval (top-k documents)
    → Context Assembly → Gemini Generation → Response
    → Emergency Classifier → Alert (if confidence ≥ 0.8)

The system uses a two-stage pipeline: first retrieve relevant medical documents via vector similarity, then generate a grounded response with the retrieved context injected into the prompt. Emergency detection runs in parallel on every query.

1. No fine-tuning — RAG was chosen over fine-tuning because medical accuracy requires traceable sources. Every response can be traced back to specific documents in the knowledge base.
2. Confidence-gated emergency detection — rather than keyword matching (which misses "my chest feels heavy"), the system uses Gemini to classify emergency severity and only triggers alerts above 80% confidence.
3. Hausa-first design — the voice input pipeline supports Hausa natively, not as an afterthought translation layer.
4. Supabase for persistence — lightweight, real-time database that handles chat history without the overhead of a full backend.

• 648 health documents indexed and retrievable
• Live deployment serving real users via Streamlit
• Sub-3 second response times for most queries
• Built and deployed during the DataFest Africa 2025 Hackathon

Try Dr. Amina live →