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Mayo Clinic's REDMOD study demonstrates that pancreatic cancer leaves detectable morphological signals in routine abdominal CT scans obtained for unrelated indications up to 18 months before a formal diagnosis. This episode examines what that finding means for cancer screening economics, workflow design, and the multimodal future of risk inference.
Part I covers the REDMOD study design, the specific morphological signals the algorithm detects, and why opportunistic screening on existing imaging is a fundamentally different economic model than dedicated screening programs. Part II examines the reimbursement pathway, the workflow integration requirements for radiologists and ordering physicians, and the competitive landscape for AI-assisted pancreatic risk detection.
