Production Deployment of Computer-Aided Detection Systems in Mammography Screening: Throughput, False Positive Reduction, and Clinical Workflow Integration

Abstract

Computer-aided detection systems for mammography have been part of clinical practice since the late 1990s, with the first generation FDA-cleared in 1998 and rapid adoption across United States screening facilities reaching a substantial majority by the mid 2010s. The first generation systems were associated with concerns about increased false positives and unclear effects on diagnostic accuracy in real-world deployment. The current generation, based on deep learning and rebranded by some vendors as AI-CAD to distinguish it from the classical CAD that preceded it, has produced results in observational studies that suggest meaningfully better diagnostic performance, although the long-term effect on patient outcomes remains the subject of ongoing prospective trials. This paper describes the production deployment of a mammographic prediction system that processed more than five thousand images per week, flagged suspicious anomalies for radiologist review, and contributed to a ten percent reduction in patient recall rates in the deployment setting. The paper focuses on the engineering decisions that determine whether such a system delivers its potential in production: throughput design, integration with the radiology workflow including PACS and reporting, false positive management, alert formatting to support rather than disrupt radiologist judgment, and the ongoing monitoring required to detect performance drift. The paper closes with a discussion of the regulatory environment, the limitations of single-site observational deployment as evidence, and the ongoing prospective trial work that is needed to confirm long-term outcomes.