AI framework integrates imaging, pathology and multi-omics for breast cancer precision medicine
The study reports a comprehensive review of artificial intelligence applications across the breast cancer care continuum, spanning imaging, digital pathology, multi-omics and clinical decision support. Investigators outline an integration framework designed to guide precision prevention, diagnosis and treatment response prediction while highlighting persistent challenges such as algorithmic bias, dataset limitations and model interpretability. For laboratory and diagnostics professionals, the review maps how AI can standardise data collection and support clinical translation across multiple biological and temporal scales. The authors note that prospective validation and robust benchmarking remain necessary before routine clinical deployment.
The original study
The role of artificial intelligence in precision medicine for breast cancer.
- Authors
- Hu JJ, Ding ZL, Yang ZC
- Journal
- Discover oncology
- Type
- Journal Article, Review
- PMID
- 42423852
Original abstract
Breast cancer (BC) ranks among the most common malignant tumors affecting women globally. The essence of precision medicine (PM) lies in "delivering the right treatment to the right patient at the right time." With advancements in artificial intelligence (AI) technologies such as deep learning (DL), breakthroughs have been achieved in analyzing data ranging from imaging to multi-omics. We review the latest applications and challenges of AI in PM for BC, offering insights for clinical practice and research. We also present an AI integration framework covering the entire BC care continuum. The framework systematically integrates multiple components, including imaging diagnosis, digital pathology, multi-omics analysis, treatment response prediction, surgical decision-making, clinical decision support, and clinical translation, thereby revealing the hierarchical mechanisms through which AI contributes to the precision management of BC. This paper reviews how AI can enable precise management of BC patients across different temporal and biological scales by collecting different types of data. Specifically, this encompasses precision prevention, diagnosis, and clinical management. It also highlights current research gaps and challenges, such as algorithmic bias, dataset comprehensiveness, and model interpretability. Ultimately, the paper offers valuable insights into the integration of AI throughout the entire process of precision medical management for BC patients.