Transforming Breast Cancer Detection and Prevention with Machine Learning: Advances, Challenges, and Future Opportunities

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, underscoring the critical need for innovative strategies in its early detection and prevention. Recent advances in machine learning (ML) have revolutionized the landscape of oncology, offering unprecedented opportunities to improve diagnostic accuracy, enhance predictive capabilities, and personalize prevention strategies. This review highlights the transformative potential of ML in breast cancer, emphasizing its applications in imaging-based diagnostics, genomic profiling, and risk stratification. Key breakthroughs, such as the integration of deep learning for histopathological image analysis, multimodal approaches combining clinical and molecular data, and the emergence of explainable AI for transparent decision-making, are explored in depth. Despite its promise, the adoption of ML in clinical practice faces significant challenges, including data heterogeneity, algorithmic bias, interpretability issues, and regulatory hurdles.