Artificial intelligence for breast cancer management.
Authors
Affiliations (9)
Affiliations (9)
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore. [email protected].
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore. [email protected].
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. [email protected].
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore. [email protected].
Abstract
Artificial intelligence is transforming breast cancer management through various machine learning applications. Artificial intelligence supports precision medicine by enhancing detection, diagnosis, prognosis, and treatment response prediction. It achieves this by analysing data from medical imaging, histopathology, genomics and multi-omics sources to improve patient recovery. This review summarises AI-driven advancements across the entire continuum of breast cancer management, spanning detection, diagnosis, prognosis, treatment and recovery. It evaluates their efficacy and limitations, explores their impact on healthcare costs and clinical practice, and addresses key challenges including generalisability, reproducibility and regulatory barriers. Evidence from recent studies highlights AI's role in improving breast cancer detection, molecular subtyping and prognostic accuracy. It also facilitates more patient-tailored therapeutic strategies and supports quality of life interventions. Nonetheless, the translation of these benefits into clinical practice requires rigorous validation, transparent model development, and equitable implementation.