AI improves imaging pathology and treatment decisions in breast cancer

Breast cancer is the most common cancer among women, with over 2.3 million new cases diagnosed each year. Traditional diagnostic methods rely heavily on human judgment, which can lead to inconsistent outcomes. Artificial intelligence (AI) offers a solution by enabling more precise and efficient diagnostic processes. AI is now being applied to various aspects of breast cancer care, including imaging, pathology, and treatment decision-making. However, AI models still face challenges such as data inconsistency and limited accessibility, requiring further development for wider implementation in healthcare systems.

A review article published (DOI: 10.20892/j.issn.2095-3941.2025.0704) in Cancer Biology & Medicine in March 2026 explores the role of AI in breast cancer care. The article, written by Jianbin Li and Zefei Jiang from the Chinese PLA General Hospital and the Academy of Military Medical Sciences, details the current applications of AI in imaging, pathology, and clinical decision-making. It also highlights the ongoing challenges and future directions for AI in oncology. AI's potential to revolutionize breast cancer management is discussed, providing key insights for both clinical practice and scientific research.

Rather than treating AI as a tool limited to image reading, the review presents it as a fast-growing framework for the entire breast cancer care pathway. It systematically tracks progress in four key areas: imaging, pathology, clinical decision-making, and drug research and development. AI is improving the reading of mammograms, ultrasound scans, and magnetic resonance imaging, while also moving pathology toward deeper case-based analysis through whole-slide image interpretation, biomarker assessment, molecular subtype prediction, and prognosis evaluation. Beyond diagnosis, AI is increasingly being used to combine clinical, imaging, and pathological data to support treatment decisions and personalized management. The review also points to a newer frontier: AI-assisted drug research, including target discovery, biomarker identification, and prediction of therapeutic response. Together, these advances show that AI is no longer a narrow technical add-on, but a systematic force reshaping how breast cancer is understood and managed.

AI has already demonstrated substantial benefits in breast cancer care, but challenges remain. Data standardization is crucial for ensuring the accuracy and reliability of AI-driven decisions. The generalization of AI models across different medical settings and patient populations is essential for ensuring that these technologies are universally effective. Addressing issues such as data privacy, regional disparities, and the need for clear ethical and legal frameworks will help ensure the safe and equitable use of AI in clinical settings.

The integration of AI into breast cancer care holds great promise for improving both the efficiency and accessibility of treatment. AI can enhance early detection, provide more personalized treatment plans, and assist in drug development. By overcoming current challenges, AI will support the broader healthcare goals of reducing disease incidence, improving survival rates, and ensuring better quality of life for breast cancer patients. As AI technologies evolve, they will continue to improve clinical decision-making, ultimately advancing personalized medicine and extending high-quality care to underserved regions.