To assess the diagnostic value of artificial intelligence(AI) system in the differential diagnosis of benign and malignant breast tumors of breast imaging reporting and data system(BI-RADS) grade 4.

A retrospective study was performed on 226 female patients with BI-RADS grade 4 breast masses from January 2018 to February 2020 at the Ultrasound Department of Qingdao Municipal Hospital. All the tumors were examined by routine ultrasonography and pathological results were obtained by operation or puncture biopsy. The AI system and breast ultrasound specialists with different years of experiences (2, 4, and 6 years) were used to analyze the breast mass ultrasound images and judge the lesion nature, and the diagnostic accuracy of the AI system and the doctors with different years of experience for breast cancer were calculated by the four-fold table method, and the χ² test was used to compare the diagnostic accuracy of AI system with that of physicians with different years of experience in breast cancer masses of different sizes.

A total of 226 breast masses were confirmed by pathology, including 96 benign lesions and 130 malignant lesions. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the AI system were 93.84%, 92.71%, 94.57%, 91.75%, and 93.36%, respectively, which were higher than those of doctors with different years of experience. There were significant differences in diagnostic accuracy between the AI system and physicians with different years of experiences (P=0.029, 0.011, and 0.002, respectively) in breast cancer masses ≤0.5 cm,＞0.5-1.0 cm, and＞1.0-1.5 cm, although there was no significant difference in diagnostic accuracy of breast cancer masses＞1.5-2.0 cm and＞2.0 cm (P=0.117 and 0.668, respectively). There were significant differences in diagnostic accuracy between the AI system and physicians with 2 years of experience (P=0.006, 0.002, and 0.001, respectively) in breast cancer masses ≤0.5 cm,＞0.5-1.0 cm, and＞1.0-1.5 cm.

The AI system has high diagnostic value in the differentiation of benign and malignant breast masses of BI-RADS grade 4, especially in the diagnosis of breast cancer whose diameter is less than 1.5 cm, and it can assist junior doctors to improve the diagnostic rate of breast cancer.