Brain Connect AI

Diagnostic errors affect approximately 12 million Americans annually, largely due to physician cognitive overload from fragmented data across electronic health records (EHRs), imaging systems, and rapidly evolving medical literature. Brain Connect solves this by deploying a collaborative multi-agent system that mirrors a hospital's diagnostic team. Instead of relying on a single LLM prompt, Brain Connect utilizes a stateful Lang Graph architecture where specialized agents handle distinct domains: 1. Clinical Agent: Parses patient history, symptoms, and lab results to extract key clinical signals. 2. Imaging Agent: Analyzes text-based radiology reports to identify anatomical anomalies and pathologies. 3. Literature RAG Agent: Queries a vector database of PubMed abstracts and medical guidelines to find relevant research, retrieving context using high-throughput embeddings. 4. Guidelines Agent: Cross-references findings against standard care protocols. 5. Consensus Orchestrator: The "Attending Physician" agent that aggregates all insights, resolves conflicting data, and generates a ranked list of differential diagnoses with confidence scores and a recommended treatment plan.