Advancing biomedical knowledge that is computable, connected, and worthy of trust.
Using biomedical technologies, health data, and informatics methods to make biomedical research more efficient, effective, and rigorous — defining and expanding the foundations and practice of clinical research informatics.
A discipline he named and defined: the systematic, ongoing surveillance of clinical AI before, during and after deployment for safety, effectiveness, and ethical impact — modeled on how medicine monitors drugs.
Building systems where everyday care and discovery feed each other, so health systems learn and improve continuously rather than episodically.
Turning the health data and biomedical evidence into structured, connected, machine-usable knowledge — governed for integrity so that what AI learns from can be trusted.
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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