Integrating multi-omics and artificial intelligence for personalized breast cancer management: A guide to clinicians

Breast cancer's (BC) diverse nature and global impact demand tailored clinical strategies. Conventional screening methods often fall short in early detection and individualized risk assessment. By merging multi-omics technologies such as genomics, transcriptomics, proteomics, and metabolomics with artificial intelligence (AI), clinicians gain powerful tools to navigate this complexity. AI's ability to analyze vast, intricate multi-omics datasets enables precise risk stratification, early diagnosis, and the development of customized treatment plans. Applications range from refining mammographic analysis and forecasting therapy outcomes to uncovering novel biomarkers. However, barriers such as data standardization, model applicability across diverse patient groups, and AI interpretability limit clinical integration. This review provides clinicians with a comprehensive guide to current advances in multi-omics profiling, including genomics, transcriptomics, proteomics, and metabolomics, as well as their integration through AI-driven models to decode tumor heterogeneity and predict treatment response. We discuss cutting-edge computational frameworks, challenges in data integration, and clinical applications that enhance prognostic accuracy and facilitate precision oncology approaches. By embracing the convergence of multidimensional molecular data and AI, clinicians can deliver individualized BC care that optimizes therapeutic outcomes and advances the post-genomic era of oncology.