Comprehensive evaluation of AI in a large regional mammography screening program for breast cancer: detection, interval cancers, and radiologist workload

Artificial intelligence (AI) has been proposed to enhance mammography screening. Published literature suggested that AI increases detection and reduces workload. No published study has investigated the prospective impact of AI on sensitivity, specificity, and interval cancers. This was a population-based study on screening quality before and after implementation of AI in screening for triage and decision support. Among 270,974 screened women (156,151 with AI-support) AI-assistance improved sensitivity (73.3% with AI vs. 69.7% before AI, P=0.049), specificity (98.3% vs. 97.9%, P<0.001), and detection rates (7.8 vs. 6.5 per 1,000, P<0.001), while reducing recall and false-positive rates. Importantly, the 2-year interval cancer ratio decreased (26.7% vs. 30.3%, P=0.049). The reading workload was reduced by 36%. Cancers detected with AI-support presented with more favorable subtypes, including higher rates of ER-positive and luminal A tumors. These results provide evidence that AI can safely and effectively improve population-based screening by earlier detection of breast cancer.