Simple SummaryWomen who have a genetic mutation of BRCA1 or BRCA2 are at a significantly higher risk for developing breast cancer. Early detection is crucial for an improved prognosis, therefore they are offered an intensive follow-up program, including a yearly MRI scan. Although MRI is the most sensitive imaging modality for breast cancer detection, it was found that a significant number of tumors are overlooked or misinterpreted, leading to a delayed diagnosis. Aiming to improve breast cancer diagnosis at early stages, we developed an artificial-intelligence based tool that is shown to classify correctly ~65% of the tumors at an early time point. These tumors were not suspected/diagnosed by the radiologists at that time point, but only at the next MRI scan. We believe that such an AI-system could serve as an aid to radiologists, improve their decision-making and achieve an ‘earlier than early’ diagnosis of breast cancer in BRCA carriers.AbstractFemaleBRCA1/BRCA2(=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered toBRCAPV carriers from age 25–30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection inBRCAPV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs ofBRCAPV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate ‘earlier than early’ BC diagnosis inBRCAPV carriers.