Role of Artificial Intelligence (AI) in Breast Cancer Screening: An Integrative Review - Adaya, Clarice; Aguilar, Jessica; Alcantara, Maria Mitzi; Marquez, Jenygrace; Narvaez, Roison Andro (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose – This paper reviews existing research on the application of artificial intelligence (AI) in breast cancer screening. It summarizes how AI contributes to early detection, diagnostic accuracy, and efficiency in imaging interpretation. Method – An integrative review was conducted using studies retrieved from Google Scholar, PubMed, CINAHL, Scopus, and ScienceDirect. Peer-reviewed, English-language articles discussing AI in breast cancer detection were included. Twenty studies published between 2019 and 2022 met the inclusion criteria. Results – The selected studies from various countries demonstrated that AI improved the detection of suspicious lesions, reduced false-positive and false-negative findings, and enhanced image-reading efficiency. AI tools applied to mammography, tomosynthesis, magnetic resonance imaging, and ultrasound provided greater consistency and reliability in identifying subtle and interval cancers. Combining AI with radiologist interpretation produced superior diagnostic performance compared to either used alone. Machine-learning models were also shown to predict recurrence and assist in individualized risk assessment, providing valuable insights for patient management and follow-up care. Conclusion – Integrating AI into breast cancer screening enhances accuracy, efficiency, and timeliness in diagnosis. Rather than replacing medical practitioners, AI should be implemented as a supportive tool that strengthens human expertise and decision-making in clinical practice. Recommendations – Healthcare institutions must consider to adopt validated AI systems with structured training, monitoring, and evaluation to ensure safe, ethical, and equitable use. Research Implications – Future research should include large, diverse populations and multicenter trials to validate outcomes, minimize algorithmic bias, and assess cost-effectiveness for sustainable integration into screening programs.