Next-gen diagnostics: artificial intelligence-powered imaging in breast cancer care

Abstract

Breast cancer is the second largest cause of mortality globally. Early detection of breast cancer may aid in better therapeutic strategies and prolong the lives of the clinical subjects. However, conventional diagnostic methods rely mainly on subjective evaluations of tumor morphology, enhancement type, and anatomic connection to the adjacent tissues. Artificial intelligence (AI) has emerged as a transformative tool in breast cancer diagnosis, particularly within medical imaging. AI-driven methods such as deep learning and radiomics have demonstrated significant improvements in mammography, magnetic resonance imaging, and ultrasound by enhancing detection sensitivity, reducing false positives, and streamlining clinical workflows. Recent advances in convolutional neural networks and hybrid architectures have enabled more accurate tumor classification, lesion segmentation, and risk stratification. While multimodal strategies integrating imaging with clinical or genomic data show promise for personalized care, the primary impact of AI lies in its ability to improve imaging-based diagnostics. This review summarizes current advances in AI for breast cancer imaging, discusses challenges related to generalizability and clinical translation, and highlights future directions for developing clinically robust diagnostic tools. The goal is to advance the development of more accurate and efficient diagnostic tools by integrating multiple imaging modalities and other patient-specific information.

Keywords

Breast cancer / cancer imaging / artificial intelligence (AI) / machine learning (ML) / deep learning / diagnosis

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