AI and Precision Medicine in Breast Cancer: Towards Truly Personalized Care

Abstract

Background: Breast cancer remains the most frequently diagnosed malignancy and one of the leading causes of cancer-related mortality among women worldwide despite remarkable advances in screening, molecular diagnostics, targeted therapies, and personalized treatment strategies. The extraordinary biological heterogeneity of breast cancer, characterized by diverse molecular subtypes, genomic alterations, tumor microenvironment interactions, hormonal influences, and immune responses, presents significant challenges to conventional population-based treatment approaches. Recent developments in artificial intelligence (AI), computational oncology, multimodal data integration, and precision medicine have transformed breast cancer management by enabling individualized diagnosis, prognostic prediction, therapeutic optimization, recurrence monitoring, and survivorship care. Artificial intelligence integrates mammography, digital breast tomosynthesis, magnetic resonance imaging, ultrasound, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory biomarkers, electronic health records, and longitudinal clinical information into comprehensive computational models capable of supporting personalized clinical decision-making. Advances in machine learning, deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced breast cancer detection, molecular classification, treatment selection, prediction of therapeutic response, toxicity assessment, and long-term disease surveillance. Despite remarkable progress, important challenges remain regarding data interoperability, computational scalability, ethical governance, cybersecurity, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of artificial intelligence-driven precision medicine in breast cancer, highlighting computational foundations, current clinical applications, emerging innovations, and future perspectives toward truly personalized breast cancer care.