Recently, in the companion to this article, Yala et al 1 reported on the validation of an artificial intelligence (AI)-aided breast cancer risk model called Mirai on the basis of traditional mammograms across multiple international cohorts. To the best of our knowledge, this is the first study suggesting that the AI-based breast cancer screening model developed on a largescale data set across seven hospitals with at least five races can deliver impressively robust transferability. Such extensive cross-regional validation is challenging because of interethnic heterogeneity of breast cancer, device bias from different institutions, and completeness of patient clinical data. 2 To overcome these challenges, this study developed a risk factor predictor that could produce risk factors referred in the Tyrer-Cuzick (TC) guidelines and then combined these factors with mammographic images. This study also leveraged a conditional-adversarial training scheme to eliminate the bias caused by different mammography machines. As a result, Mirai maintained high performance (range, 0.75-0.84) and could accurately identify high-risk subsets across all test cohorts. Moreover, this AI model shows better performance than the widely used traditional TC model in improving early detection and reducing overtreatment of breast cancer. Thus, this study concluded that Mirai has the potential to replace current breast cancer risk assessment models recommended by clinical guidelines for magnetic resonance imaging screening.