AI-Assisted Precision Medicine for Breast Cancer: Current Advances and Future Perspectives

Abstract

Breast cancer remains the most commonly diagnosed malignancy among women worldwide and continues to be a major contributor to cancer-related morbidity and mortality despite remarkable advances in screening, molecular diagnostics, targeted therapy, and precision medicine. The remarkable biological heterogeneity of breast cancer, characterized by diverse molecular subtypes, genomic alterations, tumor microenvironment interactions, and variable therapeutic responses, necessitates increasingly individualized treatment strategies. Artificial intelligence (AI) has emerged as a transformativtechnology in precision breast oncology by enabling comprehensive analysis of radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory biomarkers, electronic health records, and real-world clinical data. Machine learning, deep learning, natural language processing, computer vision, multimodal learning, graph neural networks, and foundation models have significantly improved breast cancer screening, diagnosis, molecular classification, prognostic prediction, therapeutic optimization, radiogenomics, computational pathology, and clinical decision support. AIassisted precision medicine further facilitates personalized treatment selection, adaptive therapeutic monitoring, drug discovery, survivorship care, and long-term outcome prediction. Emerging innovations including explainable artificial intelligence, digital twins, federated learning, liquid biopsy, single-cell sequencing, wearable technologies, and generative AI are expected to further transform individualized breast cancer management. Despite substantial progress, important challenges remain regarding data harmonization, interoperability, transparency, algorithmic bias, cybersecurity, regulatory approval, and equitable implementation. This review discusses the current landscape of AI-assisted precision medicine in breast cancer, highlighting technological advances, clinical applications, implementation challenges, and future perspectives for personalized oncology.