Artificial Intelligence in Breast Cancer Diagnosis, Prognosis, and Personalized Treatment: Current Applications and Future Directions
Abstract:
Breast cancer is the most frequently diagnosed malignancy among women worldwide and remains a leading cause of cancer-related mortality despite significant advances in screening and therapeutic strategies. Conven tional diagnostic and treatment approaches rely heavily on expert interpretation and population-based guide lines, which may lead to inter-observer variability, delayed diagnosis, and suboptimal personalization of care. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools capable of transforming breast cancer management by enabling automated image analysis, predictive modeling, and data-driven clinical decision support. AI-based methods have demonstrated strong performance across mul tiple stages of the clinical workflow, including screening, diagnosis, risk stratification, prognosis prediction, and treatment response assessment. This comprehensive review systematically examines the current landscape of AI applications in breast cancer diagnosis, prognosis, and personalized treatment. We discuss key machine learning and deep learning techniques, multimodal data integration strategies involving medical imaging, his topathology, and genomic information, and the clinical deployment of AI systems. Additionally, we address challenges related to explainability, bias, data quality, regulatory approval, and ethical considerations. By syn thesizing recent advances and identifying existing gaps, this review aims to provide clinicians and researchers with a clear understanding of the role of AI in advancing precision oncology for breast cancer.
