Combining artificial intelligence analysis with expert mammogram reading: Determining the optimal AI positivity cut-off point for the French population-based breast cancer screening program.

PURPOSE: Artificial intelligence (AI) is increasingly explored as a complement to radiologists in population-based breast cancer screening, yet optimal strategies for its integration remain unclear. The French program relies on systematic double reading, a model challenged by rising workload and radiologist shortages. This study evaluates the performance of an AI algorithm and examines its potential role within the existing workflow. METHODS: A retrospective cohort study included 13,186 women aged 50-74 years who underwent screening mammography in Ile-de-France between 2018 and 2019. Mammograms classified as BI-RADS 1-2 at first reading were reviewed by a second radiologist and subsequently reinterpreted in 2023 using the Transpara© AI algorithm, which assigns a 0-100 risk score. ROC curve analysis identified an optimal positivity threshold (cut-off-P), and diagnostic performance metrics were calculated. Organizational scenarios considering AI placement upstream or downstream of the second reading were explored. RESULTS: The optimal cut-off-P was 36.2, yielding an AUC of 0.78. At this threshold, AI would have detected 18 of 22 cancers-including most interval-cancers- and reduced the absolute risk of interval-cancer after a negative screen by nine points. Applied upstream of the second reading, AI could have excluded approximately 60% of mammograms classified as benign at first reading, potentially reducing second-reader workload by two thirds, but at the cost of 4 missed cancers and 4807 false-positive classifications. However, false positives remained frequent, and a few cancers detected at second reading received low AI scores. CONCLUSION: AI shows substantial potential to streamline breast cancer screening by safely triaging negative examinations before second reading. Nonetheless, limitations related to false-positives and missed cancers support a complementary-rather than substitutive- role for AI, ideally positioned between first and second readings.