Artificial intelligence–assisted breast ultrasound: modest AUROC improvement and shorter interpretation time without significant change in diagnostic accuracy

Objectives To evaluate whether Vis-BUS, a commercial artificial intelligence (AI) breast ultrasound detection and analysis software, improves diagnostic discrimination and interpretation efficiency in breast ultrasound examinations. Materials and methods This retrospective multi-reader study included 258 breast ultrasound examinations (129 malignant and 129 benign lesions). Six radiologists independently interpreted all cases without AI and, after a two-week washout, with AI assistance. Diagnostic performance metrics, including the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), accuracy, sensitivity, and specificity, were compared using multi-reader analysis. Median interpretation time per case was recorded and compared using paired statistical tests. Results Vis-BUS assistance modestly increased the pooled AUROC (0.921 vs. 0.953, p = 0.002) and reduced median reading time (6.0 vs. 3.0 s, p < 0.001), whereas AUPRC, accuracy, sensitivity, and specificity did not differ significantly (all p > 0.06). Accuracy (79.1% vs. 83.9%, p = 0.061), sensitivity (94.2% vs. 96.3%, p = 0.243), and specificity (64.0% vs. 71.6%, p = 0.069) showed no significant differences. Median interpretation time decreased from 6.0 to 3.0 s ( p < 0.001). Subgroup analyses demonstrated significant AUROC improvements for dense breasts and tumors ≤ 2 cm ( p < 0.001 for both). Conclusion Vis-BUS AI assistance was associated with improved diagnostic discrimination and shorter interpretation time. However, accuracy, sensitivity, and specificity did not differ significantly. These findings suggest potential efficiency benefits, while the clinical impact remains to be confirmed in prospective multi-center studies.