Unlocking AI-Accelerated Biomedical Discovery through Federated Data Networks

Biomedical research generates vast amounts of valuable data such as genomic sequences, high-resolution medical images, electronic health records, and diverse omics profiles. Yet most datasets remain siloed due to incompatible platforms and restrictive governance (e.g., HIPAA, GDPR), which prevents integration and reuse. We propose a federated, FAIR-compliant benchmarking network that links diverse biomedical resources via delegated governance and model-to-data (MTD) evaluation. Building on platforms such as Synapse and MedPerf, the network enables privacy-preserving training and evaluation across globally distributed datasets, returning only performance metrics. This infrastructure will catalyze global-scale AI discovery, improve generalizability, and reduce bias by evaluating methods across heterogeneous, real-world data without moving any protected records.