To preliminarily investigate the diagnostic efficacy of an automated artificial intelligence (AI) detection system for small breast masses, as well as its complementary role for physicians with varying levels of experience.

A total of 164 pathologically confirmed small breast masses with a diameter of up to 10 mm were chosen at Hwa Mei Hospital, University of Chinese Academy of Sciences, and four physicians (doctors A and B were senior physicians, and doctors C and D were junior ones) with varying levels of experience independently diagnosed the masses according to the Breast Imaging Reporting and Data System (BI-RADS) classification, and the results were reported as groups A1, B1, C1, and D1. Four weeks later, the four physicians again jointly applied the AI automatic detection system to diagnose the masses, and the results were reported as groups A2, B2, C2, and D2. The results of the AI automatic detection system were reported as group M. Taking pathological results as the gold standard, the sensitivity, specificity, accuracy, negative predictive value, and positive predictive value of diagnosis by physicians in different groups were calculated, and the receiver operating characteristic curve was drawn. The Kappa test was used to calculate the interobserver agreement between groups.

Pathological results showed that among the 164 breast masses, 117 (71.34%) were benign and 47 (28.66%) were malignant. The AI automatic detection system had good diagnostic efficacy for small breast lumps, with a sensitivity, specificity, and accuracy of 91.49%, 90.6%, and 90.85%. The diagnostic efficacy of senior physicians after the joint application of the AI automatic detection system tended to increase, with a sensitivity and specificity of 82.98% and 82.05% in group A1; 87.23% and 89.74% in group A2; 80.85% and 84.62% in group B1; and 85.11% and 89.74% in group B2. The diagnostic efficacy of senior physicians with the aid of the AI automatic detection system was significantly improved, with a sensitivity and specificity of 76.60% and 74.36 in group C1; 82.98% and 82.05% in group C2; 68.09% and 73.50% in group D1; and 80.85% and 80.34% in group D2. The interobserver agreement of BI-RADS classification of small breast masses by low senior physicians with the help of the AI automatic detection system was significantly higher. The Kappa value among junior physicians increased from 0.236 to 0.549. The Kappa value among junior and senior physicians increased from 0.268-0.284 to 0.432-0.540.

The AI automated detection system has high diagnostic efficacy in determining the benignity and malignancy of small breast masses, and its adjunctive effect varies among experienced physicians, with a greater impact on junior physicians than on senior physicians. The AI automated detection system helps to improve the interobserver agreement of BI-RADS classification among junior physicians.