Breast cancer research is being reshaped by AI. Here are four compelling papers from Intelligent Oncology:
☆ A novel deep learning approach that automatically calibrates weights to improve HER2 status detection directly from whole-slide images—potentially reducing inter-observer variability and speeding up pathology workflows.
Wang S, Guo X, Ma J, et al. AWCDL: Automatic weight calibration deep learning for detecting HER2 status in whole-slide breast cancer image. Intelligent Oncology. 2025;1(2):128-138. doi:10.1016/j.intonc.2025.03.008
☆ This perspective piece bridges the gap between AI's theoretical potential and real-world clinical implementation, addressing workflow integration, screening accuracy, and the critical issue of data privacy.
Ning SGS, Hartman M. From Promise to Practice: Harnessing Artificial Intelligence for Breast Cancer Screening. Intelligent Oncology. 2025;1(1):4-6. doi:10.1016/j.intonc.2024.11.001
☆ Using machine learning to integrate multi-omics data, this study uncovers new breast cancer subtypes that could pave the way for more precise prognosis and tailored treatment strategies.
Wang T, Wu L, Gao Y, et al. Integrative multi-omics clustering for identifying novel breast cancer subtypes with distinct molecular and clinical characteristics. Intelligent Oncology. 2026;2(1):100033. doi:10.1016/j.intonc.2025.12.001
☆ A thorough review of how combining multiple imaging modalities (MRI, ultrasound, mammography, etc.) with AI enhances diagnostic accuracy and offers a holistic view of tumor biology.
Wei TR, Yan Y. Multimodal medical imaging AI for breast cancer diagnosis: A comprehensive review. Intelligent Oncology. 2026;2(1):100037. doi:10.1016/j.intonc.2025.12.005
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