An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions

Breast cancer diagnosis using magnetic resonance imaging remains limited by high false-positive rates and substantial inter-reader variability, especially for lesions classified as Breast Imaging Reporting and Data System (BI-RADS) category 4, often leading to unnecessary biopsies. Here we show that the BI-RADS 4 Lesions Analysis System (BL4AS), an artificial intelligence system powered by foundation models and leveraging the rich spatiotemporal information of dynamic contrast-enhanced MRI, addresses these diagnostic challenges. Developed on a multicenter dataset of 2,803 lesions from 2,686 female patients, BL4AS demonstrates robust performance with areas under the curve of 0.892-0.930 and significantly outperforms radiologists in specificity (0.889 versus 0.491). BL4AS-assisted interpretation significantly improves diagnostic accuracy for both senior and junior radiologists, reducing inter-reader variability by 24.5% and decreasing false-positive rates by 27.3%. BL4AS further stratifies lesions into subcategories (4 A, 4B and 4 C) for refined risk assessment, offering a practical tool for precision breast cancer management. Diagnosing breast cancer through MRI is limited by high false-positive rates and inter- reader variability, leading to unnecessary biopsies. In here, the authors find that the BI-RADS 4 Lesions Analysis System (BL4AS) model improves diagnostic accuracy and reduces unnecessary biopsies as well as inter-reader variability