Artificial Intelligence in Breast Cancer: Innovations in Screening, Diagnosis, and Treatment

Abstract

Breast cancer remains the most frequently diagnosed malignancy among women worldwide and continues to be a leading cause of cancer-related mortality despite substantial advances in screening programs, molecular diagnostics, targeted therapies, and precision medicine. Early detection and individualized treatment remain essential for improving survival while minimizing treatment-related morbidity. Recent developments in artificial intelligence (AI) have transformed breast cancer care by enabling automated analysis of radiological imaging, digital pathology, genomic sequencing, electronic health records, and multimodal clinical data. Machine learning, deep learning, computer vision, natural language processing, multimodal learning, graph neural networks, and foundation models have demonstrated remarkable capabilities in breast cancer screening, lesion detection, risk prediction, molecular classification, treatment optimization, prognostic assessment, and survivorship care. AI-powered systems now support mammographic interpretation, breast ultrasound, magnetic resonance imaging, digital pathology, radiogenomics, surgical planning, radiation therapy, immunotherapy prediction, and clinical decision support. Emerging technologies including explainable artificial intelligence, federated learning, generative AI, wearable health technologies, and digital twins are expected to further enhance precision breast oncology. Nevertheless, important challenges remain regarding data quality, algorithmic bias, interoperability, cybersecurity, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of artificial intelligence applications in breast cancer, highlighting current innovations, clinical applications, emerging technologies, and future directions for precision breast cancer management.