Artificial Intelligence in Breast Cancer: Advancing Diagnosis and

Treatment

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.