Abstract

Artificial Intelligence (AI) and machine learning (ML) are revolutionizing breast cancer research and clinical care by enabling advanced analysis of complex datasets. This review explores the diverse applications of AI and ML in breast cancer, encompassing early detection, diagnosis, prognosis, treatment optimization, and patient management. AI algorithms applied to imaging modalities, such as mammography and MRI, enhance detection sensitivity and accuracy, potentially leading to earlier intervention and improved survival rates. ML techniques in pathology aid in tumor classification, molecular subtype prediction, and assessment of tumor aggressiveness, supporting personalized treatment strategies. Predictive models integrate multi-omics data to forecast patient outcomes, recurrence risks, and treatment responses, guiding clinicians in optimizing therapeutic approaches. AIdriven decision support systems facilitate precision medicine by tailoring treatment plans based on individual patient characteristics and biomarker profiles. Methodological challenges include data quality, interpretability of AI models, and ethical considerations surrounding patient data privacy. Future directions focus on integrating multi-modal data sources, advancing real-time analytics, and leveraging AI for drug discovery in breast cancer. Collaborative research efforts are essential to validate AI applications and translate innovations into clinical practice, ultimately improving outcomes and quality of life for breast cancer patients globally.