Healthcare RAG Summarizer
A retrieval-augmented clinical summarization pipeline for notes and patient records.
- Python
- FastAPI
- Docker
- Pinecone
- LangChain
This project delivers an AI summarization engine that combines vector search with LLM prompts to create concise clinical notes and visit summaries. The pipeline improves information retrieval accuracy and supports clinicians with context-aware decision support.
What it does
The service ingests clinical documentation and stores embeddings in a vector database. During inference, it uses RAG techniques to retrieve relevant context, then synthesizes clean summaries, SOAP notes, and patient history briefings for provider review.
My role
I built the end-to-end pipeline and API surface, including the vector ingestion process, prompt orchestration, and backend integration. I also led evaluation and performance tuning to ensure latency and relevance met clinical-grade production requirements.