This shift is transforming genAI from an experimental capability into an auditable system of record. The most forward-looking health systems already maintain AI registries (similar to software bills of materials) listing approved models, data sources and governance owners. By next year, that practice will be standardized or well on its way.
How this looks in the real world
Consider the challenge of managing a patient with chronic conditions such as diabetes and heart failure. Their data spans years of lab results, imaging, prescriptions and clinical notes scattered across multiple EHRs. The old approach would be to dump the entire record into an LLM and ask, “What should happen next?”
A modular, multi-agent approach works differently. An extraction agent structures the patient’s history, a reasoning agent identifies risk patterns, a medication-review agent flags contraindications, and a conversational agent explains the findings to clinicians in plain language. A governance layer tracks every inference, ensuring transparency and auditability.
