MedScholar

MedScholar

An advanced agentic medical research and decision-support platform that utilizes multi-agent RAG to synthesize authoritative clinical insights for healthcare professionals.

Client Needs

Clinicians and medical researchers are overwhelmed by the sheer volume of new medical literature and the fragmentation of clinical data. Crucial insights are split between unstructured research papers and highly structured databases (such as drug registries and disease graphs). Connecting these disparate sources manually is slow and error-prone, while traditional keyword search engines lack the reasoning capability to synthesize safe, context-aware clinical answers in real time.

Working Process

1. 01Architected a multi-agent AI pipeline where a master agent analyzes complex medical queries and determines the optimal retrieval path across semantic search and graph databases.
2. 02Deployed autonomous worker agents to query, extract, and cross-reference data simultaneously from PubMed, RxNorm, and OpenFDA, along with private patient case repositories.
3. 03Integrated a hybrid retrieval engine combining medical knowledge graphs with vector-based text search to map symptoms, disease pathways, and drug-to-drug interactions with high contextual accuracy.
4. 04Built a unified web and mobile application (iOS & Android) from a single React Native codebase, integrating an over-the-air (OTA) update system to ship critical clinical features and security patches instantly.

Check & Launch