06Clinical AI · Knowledge Graphs

Multimodal Clinical AI Assistant

GPT-4V vision, FHIR parsing, Neo4j patient graphs, and vector search combined for knowledge-graph patient profiling.

94%
retrieval accuracy
15+
biomarker categories
87%
anomaly sensitivity
3
pilot healthcare teams

In short

  • Longitudinal biomarker-trend layer via graph traversal — 87% anomaly-detection sensitivity, 16 points above rule-based baselines.
  • HIPAA-aligned microservices on AWS EKS; adopted by 3 pilot healthcare teams.
01

Approach

GPT-4V for document and image understanding, FHIR parsing for structured clinical records, Neo4j patient graphs for relationships, and vector search for semantic recall — combined into knowledge-graph patient profiling at 94% retrieval accuracy across 15+ biomarker categories.

02

Longitudinal signal

A biomarker-trend layer built on graph traversal, so a value is read against that patient's own history rather than a population threshold. 87% anomaly-detection sensitivity, 16 points above rule-based baselines.

03

Deployment

HIPAA-aligned microservices on AWS EKS, adopted by 3 pilot healthcare teams.