You Can’t Have AI Without Interoperability—And You Can’t Scale Interoperability Without AI
AI in healthcare is advancing rapidly—but its effectiveness is fundamentally constrained by fragmented, inconsistent data. Interoperability provides the structured, normalized foundation AI depends on, yet traditional approaches to interoperability remain slow, expensive, and difficult to scale.
This session explores the emerging symbiotic relationship between interoperability and AI. Let’s discuss how interoperable data unlocks high-quality AI, and how AI—through agentic systems, automation, and continuous learning—can dramatically accelerate interoperability itself.
This talk introduces a new model: interoperability as a self-improving system, where AI and deterministic logic work together to continuously enhance data quality, reduce integration effort, and scale across standards like HL7v2, FHIR, and DICOM.
Speakers
Chami Rupasinghe is an experienced Product Leader at Microsoft working on the next generation of healthcare interoperability and AI systems. He focuses on rethinking how data moves across healthcare—evolving from static pipelines to intelligent, agent-driven systems that continuously improve. With a background spanning product, data science, and cloud platforms, and over a decade of experience leading engineering teams, Chami works at the intersection of AI, interoperability, and platform design to enable trustworthy, scalable healthcare intelligence.