Artificial Intelligence in Early Breast Cancer Detection: A Systematic Review of Innovations in Preventive Women's...

Background: Breast cancer (BC) remains one of the leading causes of cancer-related deaths worldwide, with early detection being essential for improving survival rates, treatment outcomes, and preventive women's healthcare strategies. Artificial Intelligence (AI), particularly deep learning (DL) and machine learning (ML) algorithms, has emerged as a promising tool for improving the accuracy and efficiency of BC diagnosis. This systematic review explores the role of AI in early BC detection and its implications for preventive and patient-centered women's healthcare. Methods: A comprehensive search was conducted in PubMed and Scopus for studies published between January 2015 and December 2025, following PRISMA guidelines. The search strategy included combinations of MeSH terms and free-text keywords related to artificial intelligence, machine learning, deep learning, BC screening, mammography, magnetic resonance imaging (MRI), ultrasound, and BC detection. Eleven studies involving approximately 148,170 participants were included. Methodological quality was assessed according to study design. Results: AI-driven diagnostic systems demonstrated improved accuracy, sensitivity, specificity, and efficiency compared with conventional approaches. AI applications in mammography and ultrasound reduced radiologists' workload and healthcare costs while enhancing cancer detection rates, particularly in women with high breast density. AI models also showed potential in identifying metastases and predicting clinical outcomes, supporting more efficient patient management and follow-up care. Conclusions: AI-based tools represent a promising advancement in BC detection and screening efficiency. Their integration into BC screening programs may strengthen preventive women's healthcare services and improve patient outcomes. However, further large-scale clinical validation and real-world implementation studies are required before widespread clinical implementation.