Technology & Architecture
Built on retrieval-augmented generation (RAG) to keep clinical answers grounded in verified source material
System Architecture
MediAssist uses a retrieval-augmented generation architecture designed specifically to prevent hallucination and ensure every answer is traceable to source documents.
When a clinician asks a question, the system:
- Converts the query into vector embeddings
- Performs hybrid search (vector + keyword) across the protocol knowledge base
- Reranks results by clinical relevance
- Passes the most relevant fragments to an LLM as context
- Generates an answer strictly grounded in the provided context
- Returns the answer with complete source citations
Technology Stack
Vector Database
Protocol embeddings and hybrid search powered by PostgreSQL with pgvector extension
Infrastructure
Cloud infrastructure running on AWS for scalability and reliability
Authentication
Identity and access management through Auth0 for provider-independent auth
Frontend
Web application and mobile apps built with React ecosystem
Backend
API services handling protocol processing, retrieval, and generation
Search & Retrieval
Hybrid search combining semantic similarity and keyword matching with reranking
Design Principles
Grounded Generation: Answers must be traceable to specific protocol sections. The system never generates information not present in the source material.
Source Traceability: Every response includes citations with protocol name, section, and page numbers so clinicians can verify the information.
Separation of Concerns: Demo environments use isolated datasets and never connect to production institutional data.
Scalability: Architecture designed to scale from pilot institutions to national-level protocol repositories.