Why Most Radiology AI Fails: Workflow, Governance & Integration Problem with Tessa Cook

Episode notes

Advanced engineering and deep learning can create breathtaking diagnostic models, but how do we successfully integrate complex AI tools into a high-volume, multi-site hospital system without creating more chaos? In this episode of Frame by Frame: Rethink Imaging, host Chris St. John sits down with Dr. Tessa Cook, a cardiovascular radiologist, national leader in imaging informatics, and Commission Chair for Informatics for the American College of Radiology (ACR), to break down the reality of clinical technology adoption. Dr. Cook explains the profound structural difference between a model that performs well in a retrospective testing lab and one that actually adds value to a radiologist’s busy shift. She shares concrete case studies detailing how her team built "Arnie" (Automated Radiology Recommendation Tracking Engine) to bridge fragmented patient care, ultimately giving rise to Penn Medicine's enterprise-wide High-Risk Follow-Through program that caught 12 early-stage cancers in its initial phase.

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