Breast MRI is a highly sensitive modality for detecting breast cancer, but interpretation is often time-consuming and requires significant expertise. Recent advances in artificial intelligence (AI) and deep learning have enabled automated lesion detection, characterization, and risk stratification. This review discusses the current state of AI integration in breast MRI, including convolutional neural networks for lesion segmentation, radiomics-based predictive modeling, and computer-aided diagnosis tools. Clinical studies demonstrate that AI-assisted interpretation improves diagnostic accuracy, reduces inter-observer variability, and accelerates workflow. Challenges include dataset standardization, generalizability of models, and integration into clinical practice. Future research should focus on multi-center validation and AI-guided personalized treatment planning.

Keywords: Artificial Intelligence, Breast MRI, Diagnostic Imaging, Breast Cancer