Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads to inaccuracies in rare cases. In this study, we address a long-standing challenge of improving the diagnostic model performance on rare cases using long-tailed data. Specifically, we introduce a pipeline, TAILOR, that builds a knowledge-driven generative model to produce tailored synthetic data. The generative model, using 3,749 lesions as source data, can generate millions of breast-US images, especially for error-prone rare cases. The generated data can be further used to build a diagnostic model for accurate and interpretable diagnoses. In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33.5% in specificity with the same sensitivity, improving their performance by providing predictions with an interpretable decision-making process. Moreover, on ductal carcinoma in situ (DCIS), our diagnostic model outperforms all radiologists by a large margin, with only 34 DCIS lesions in the source data. We believe that TAILOR can potentially be extended to various diseases and imaging modalities. 1 Main Breast cancer has become the most common cancer among women globally [1-3], and early detection These authors carried out this work as interns at Yizhun Medical AI Co., Ltd. The distribution of pathological subtypes is long-tailed in our training set which has 1,387 biopsy-confirmed lesions. In benign lesions, the two most frequent subtypes together account for 49.7% of the lesions, with the remaining 13 subtypes comprising 50.3%. In malignant lesions, the most frequent subtype accounts for 81.8% of the lesions, while the remaining 15 subtypes comprise only 18.2%. In breast cancer detection, ultrasound (US) is an essential imaging method widely adopted worldwide for its safety and low cost [5-7].