Artificial Intelligence in Breast Cancer: Advancing Diagnosis andTreatment

Abstract: Breast cancer remains the second leading cause of cancer-related deaths among women, with 313,510 new cases anticipated in the U.S. in 2024. It arises from abnormal cell growth driven by genetic mutations (e.g., BRCA1/BRCA2), hormonal imbalances, obesity, smoking, and alcohol use. Early detection through mammography, ultrasound, MRI, and biopsies is vital but often limited by false positives and negatives. Artificial Intelligence (AI) has emerged as a transformative tool in the diagnosis and treatment of breast cancer. Deep learning (DL) models, including Convolutional Neural Networks (CNNs), support accurate interpretation of mammograms, pathology reports, and genomic data, reducing diagnostic errors and unnecessary biopsies. Beyond detection, AI plays a significant role in treatment planning by predicting therapeutic responses, stratifying risk, and personalizing care based on genetic and imaging biomarkers. AIdriven tools such as CAD systems, volumetric breast density software, and MRI interpretation platforms assist clinicians in precise assessment and decision-making. AI also contributes to identifying high-risk individuals, particularly BRCA mutation carriers, enabling preventive interventions. Emerging research demonstrates AI’s superiority over traditional risk models in predicting recurrence and estimating treatment outcomes. This review explores key AI applications in breast cancer detection and treatment, emphasizing deep learning's evolving role in enhancing patient care.