The Impact of AI in Breast Cancer

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, demanding continuous innovation in prevention, diagnosis, and treatment. This review explores the transformative role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in addressing these challenges. AI-driven models have demonstrated remarkable potential in enhancing early detection accuracy through mammogram interpretation, reducing false negatives and positives, and supporting radiologists in diagnosis. Preventative applications, such as carcinogen identification systems (e.g., Metabokiller, ProTox-II) and genetic risk prediction models for BRCA mutations, illustrate AI’s ability to identify high-risk individuals and environments before disease onset. In treatment, AI contributes to precision medicine by predicting patient responses to chemotherapy, immunotherapy, and targeted drug therapies, enabling the development of personalized treatment plans. Emerging DL frameworks also aid in image reconstruction, biomarker identification, and noninvasive prognostics, promoting cost-effective and patient-centered care. Despite these advancements, ethical challenges—such as data bias, limited dataset diversity, and patient privacy—remain barriers to clinical integration. The study concludes that with improved data transparency and regulatory oversight, AI could revolutionize breast cancer management through enhanced accuracy, accessibility, and individualized medical care.