Multi-Agent Multimodal Data Analysis on AWS – Part 1: Data Governance and Visualization

This article demonstrates how to build a multi-agent multimodal data analysis system for healthcare using AWS services, focusing on data governance and visualization for cardiovascular disease risk evaluation.

• Ingest clinical (FHIR), imaging (DICOM), and genomic (VCF) data into purpose-built AWS services: HealthLake, HealthImaging, and S3 Tables
• Establish unified data governance using Amazon SageMaker Catalog for cross-modal discovery, access control, and lineage tracking
• Create data products grouping related assets and enable subscription workflows with row/column-level access restrictions
• Build interactive dashboards with Amazon Quick for population and patient-level insights across all data modalities
• Use Model Context Protocol servers to enable AI agents to query governed data for natural language analytics

This framework enables healthcare organizations to synthesize multimodal patient data across specialties, moving from siloed analysis to holistic, actionable clinical insights with proper governance and compliance.