Artificial intelligence (AI) is emerging as a powerful force in breast cancer research, but the future of personalized care will not be written by algorithms alone. A new special issue of Cancer Biology & Medicine brings together reviews, perspectives, editorials, and original research that examine how AI is beginning to support precision oncology while also highlighting wider advances in diagnostics, therapeutics, translational science, and clinical strategy. By placing AI alongside broader breast cancer research progress, the collection offers a more balanced and realistic picture of how the field is evolving toward more individualized care.
Breast cancer remains one of oncology's most complex diseases because of its biological heterogeneity, variable treatment responses, and rapidly changing therapeutic landscape. AI has drawn growing attention for its potential to improve prediction, classification, and treatment planning, especially as clinical, imaging, and molecular data become more interconnected. Yet progress in breast cancer medicine is also being driven by novel therapeutic modalities, precision diagnostics and subtyping, and translational oncology. A meaningful view of the field therefore requires attention not only to digital tools, but also to the scientific and clinical advances shaping care more broadly. Based on these challenges and opportunities, deeper exploration is needed into how AI and broader breast cancer research can move forward together.
Published on March 15, 2026 in Cancer Biology & Medicine, the special issue (DOI: https://www.cancerbiomed.org/content/23/3) "Harnessing Artificial Intelligence for Personalized Breast Cancer Treatment" was guest-edited by Professor Zefei Jiang of the Department of Oncology at the Chinese PLA General Hospital. The issue reflects a wider landscape than its title alone may suggest. Alongside articles focused directly on AI in breast cancer, the collection also features work on precision immunotherapy, antibody–drug conjugates, engineered cell therapies, multi-omic analysis, digital pathology, and clinical trial methodology.
The issue's AI-related contributions show where computational approaches are already gaining traction. A review on AI in breast cancer surveys current applications and advances, while another article examines the role of radiomics in predicting response to neoadjuvant chemotherapy. A multicenter original study extends that direction by using multimodal AI to predict PIK3CA mutation status from digital pathology and clinical data. Another study applies deep learning to dynamic optical coherence tomography for label-free diagnosis of lymph node metastasis. Beyond AI, the collection also features translational studies on dual therapeutic targets in luminal androgen receptor triple-negative breast cancer, and metabolic engineering of SLC38A2 to enhance CAR-macrophage antitumor activity, collectively showing how personalized breast cancer care is being shaped by converging advances across multiple domains.
"This collection shows that the next phase of breast cancer research is not about choosing between AI and traditional oncology advances," the issue suggests. "It is about learning how to connect computational tools with stronger biological insight, better therapies, and clinically meaningful research design." That balance is what makes the special issue especially timely: AI is presented as an important enabler of precision care, but not as a substitute for the therapeutic, translational, and methodological progress that continues to drive the field.
The broader message of the issue is clear. Personalized breast cancer treatment will likely depend on a more integrated research model, one that combines computational prediction with deeper molecular understanding, better treatment strategies, and stronger clinical evaluation. By bringing AI-centered work together with studies on advances in biological discovery and therapeutic innovation, Cancer Biology & Medicine offers readers a timely snapshot of a field moving toward more precise and more connected care.
For more details and to access the full special issue, visit Cancer Biology & Medicine online .
DOI: https://doi.org/10.20892/j.issn.2095-3941.2026.0018
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