Objective To evaluate the assistant diagnostic value of S‐Detect artificial intelligence system in differential diagnosis of benign and malignant breast tumors . Methods Clinical data and ultrasound images of 201 patients undergoing breast ultrasound examination in Tongji Hospital from M arch 2018 to M ay 2018 were acquired . Two‐dimensional grayscale and color Doppler ultrasound images ,S‐Detect mode images and elastographic images of 220 breast lesions were analyzed . T he BI‐RADS categories of each lesion were divided into two groups :experienced group and random group .And according to w hether to refer to S‐Detect diagnostic results ,the BI‐RADS categories in experienced group were divided into A 1 group and P1 group .In additional ,the highest and lowest categories of the same tumor in random group were A 2 group ,and the diagnostic results of A 2 group combining with S‐Detect system were belonged to P2 group . T he ROC curves were plotted and the area under the curve ,sensitivity ,specificity or the accuracy of the different groups were compared . Agreements of diagnostic results between different groups were analyzed by Kappa test . Results Out of 220 breast lesions ,181 lesions were benign and 39 lesions were malignant . The S‐Detect artificial intelligence system had a relatively high diagnostic efficiency ,and the sensitivity , specificity and accuracy of S‐Detect classification were 92 .3% ,90 .6% ,90 .9% , respectively . With its assistance ,the specificity and accuracy in the experienced group had an increasing trend ( A 1 group :86 .7% , 88 .6% ; P1 group :91 .2% ,92 .3% ) ,and the diagnostic accuracy in random group was significantly improved ( A2 group :63 .6% －85 .5% ; P2 group :93 .2% －94 .1% ) . Both S‐Detect system and elasticity score helped to improve the efficacy of ultrasound physicians in differential diagnosis of benign and malignant breast lesions . But there were differences in diagnostic performance and assistant diagnostic ability between the two techniques . Conclusions S‐Detect technique contributes to the augment of diagnostic accuracy of ultrasound doctors in identifying breast cancer , improves the quality of random breast ultrasound examinations ,and reduces missed diagnosis and misdiagnosis of breast examinations .