AI as a Safety Net Reader for Mammograms Classified as Normal or Benign in the French Screening Program.

In the French national breast cancer screening program, second reading is performed only for mammograms interpreted as negative (Breast Imaging and Reporting Data System [BI-RADS] 1-2) at first reading, representing a unique screening workflow in Europe. This retrospective study assessed whether artificial intelligence (AI) could identify a subgroup of negative screening mammograms that could safely bypass second reading. A total of 55 589 screening mammograms from 42 419 women aged 50-74 years (January 2015-December 2019) whose mammograms were initially classified as BI-RADS 1-2 were analyzed. Second reading outcomes were compared with those of a commercial AI system using a predefined binary threshold (≥5). Among these examinations, 183 of 55 589 (0.33%) were recalled at second reading, yielding 12 cancers (positive predictive value, 6.6%; cancer detection rate, 0.22 per 1000 examinations). AI classified 42 606 of 55 589 (76.6%) examinations as low risk (≤4) and 12 983 of 55 589 (23.3%) as nonlow risk (≥5). One cancer was detected in the AI-low group (one of 55 589 [0.002%]) compared with 11 in the AI-nonlow group (11 of 55 589 [0.020%]; P < .001). Interval cancer rates were higher in the AI-nonlow group than in the AI-low group (2.16 vs 0.47 per 1000 examinations). These findings suggest that excluding AI-low examinations from second reading could reduce workload by approximately 77% while focusing radiologist review on higher-risk cases; prospective validation is needed. Keywords: Mammography, Breast, Computer Aided Diagnosis (CAD) Supplemental material is available for this article. © RSNA, 2026 See also commentary by Yao and Chae in this issue.