Advancing effective health AI, real-world evidence, and learning health systems — built on trustworthy biomedical knowledge.
Using health data, biomedical technologies, and informatics methods to make research faster, more rigorous, and more reproducible — and to generate the real-world evidence that drives discovery and translation. He helped establish clinical research informatics as a foundational discipline of modern biomedicine.
He coined and defined algorithmovigilance: the systematic monitoring of AI-driven health care for safety, effectiveness, equity, and performance over time. The goal is to make AI in medicine not only innovative, but observable, governable, and worthy of trust.
Building systems in which care, research, evidence, and improvement reinforce one another — so health systems learn continuously, responsibly, and at scale.
Transforming health data, biomedical evidence, and clinical expertise into structured, connected, machine-usable knowledge — governed for integrity so that people, institutions, and AI systems can rely on it.
For more than a decade, the closing keynote of the AMIA Informatics Summit — a curated tour of the year’s most notable clinical research informatics scholarship, with search strategy, bibliography, and slides for each year.
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