Artificial Intelligence in Breast Imaging and Screening: Current Evidence, Applications, and Future Directions

Artificial intelligence (AI) has emerged as a clinically significant and rapidly evolving technology in breast imaging, with applications spanning cancer detection, risk prediction, workflow optimization, and supplemental imaging modalities. The evidence base has matured rapidly, transitioning from retrospective accuracy studies to prospective randomized controlled trials. This narrative review synthesizes the most recent evidence (2023–2026) on AI applications in breast cancer screening and imaging, including landmark trial results, systematic reviews, and society recommendations. Current data demonstrate that AI-supported mammography screening can increase cancer detection rates by 10 to 29% and reduce interval cancer rates by up to 12% while reducing radiologist workload by 31 to 64% in double-reading screening settings, without increasing false-positive rates. However, the evidence base remains predominantly derived from nondiverse, high-income country populations, and long-term outcome data including breast cancer mortality are not yet available. Challenges related to generalizability, algorithmic bias, overdiagnosis, and regulatory frameworks remain. As the field moves toward clinical implementation, rigorous post-market surveillance, diverse dataset validation, and cost-effectiveness analyses will be essential.