ARTIFICIAL INTELLIGENCE -DRIVEN ADVANCES IN BREAST CANCER DETECTION, DIAGNOSIS AND PERSONALIZED TREATMENT (Eureka Journal of Health Sciences & Medical Innovation, 2026)
Breast cancer remains the most prevalent malignancy and a leading cause of cancer-related mortality among women worldwide. Despite significant progress in screening and therapeutic strategies, challenges such as late-stage diagnosis, tumor heterogeneity, therapeutic resistance, and inter-observer variability continue to impede optimal clinical outcomes. Recent advancements in artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), radiomics, and explainable AI (XAI), have demonstrated substantial potential in transforming breast cancer detection, diagnosis, prognosis, and treatment personalization. This review provides a comprehensive overview of current developments in AI-assisted breast cancer management, with a focus on image-based diagnostic modalities including mammography, ultrasound, magnetic resonance imaging, and automated breast ultrasound systems. Additionally, emerging applications of AI in biomarker identification, survival prediction, immunotherapy guidance, nanotechnology-based drug delivery, and resistance mitigation are discussed. While AI-driven approaches have shown promising improvements in diagnostic accuracy, efficiency, and decision support, challenges related to data heterogeneity, model generalizability, interpretability, and clinical integration persist. Addressing these limitations through large-scale validation, multidisciplinary collaboration, and standardized methodologies is essential for translating AI innovations into routine clinical practice and advancing precision oncology in breast cancer care.
Keywords: Breast cancer, artificial intelligence, deep Learning, machine learning, radiomics, computer-aided diagnosis, medical imaging, precision medicine, early detection.