Application of AI aigorithms in breast cancer screening

Breast cancer (BC) represents a major global health burden, accounting for over 20% of all malignant neoplasms in women worldwide. Mammographic screening has been established as the cornerstone for early disease detection, with its mortality reduction efficacy validated through numerous large-scale trials. The double-reading paradigm, recognized as a quality assurance standard in screening programs, enhances detection rates of early-stage malignancies while reducing interpretive errors. However, growing healthcare demands and workforce constraints – including radiologist shortages, occupational burnout, and geographic disparities in specialist distribution – challenge the sustainable implementation of this resource-intensive approach. Artificial intelligence (AI) emerges as a transformative technology to augment screening efficacy, offering solutions for workflow optimization and diagnostic standardization. This comprehensive review examines current advances in AI applications for mammographic interpretation, focusing on decision support systems, automated image analysis, and potential integration as an independent reader. We further critically evaluate technical limitations, implementation challenges, and strategies for incorporating AI algorithms into existing clinical pathways to address workforce gaps and ensure consistent diagnostic quality across healthcare tiers.