Objective To explore the diagnostic value of conventional ultrasound, shear wave elastography (SWE), and artificial intelligence (AI) for differentiating benign and malignant breast nodules and to compare their diagnostic performance. Methods A total of 306 patients with breast nodules (comprising 306 lesions) treated at our hospital from Jun. to Dec. 2023 were retrospectively enrolled. All were females, aged 28-86 years, with a mean age of (54±14) years. All patients underwent conventional ultrasound, SWE, and AI-assisted ultrasound diagnosis. The conventional ultrasound features and maximum elasticity value (Emax) were recorded for each nodule. With pathological results as the gold standard, binary logistic regression was performed to construct diagnostic models. Receiver operating characteristic curves were plotted for each diagnostic method, and the area under curve (AUC) values were compared to evaluate differences in diagnostic performance. Results For SWE, the AUC of Emax for differentiating benign and malignant nodules was 0.71, with an optimal cut-off value of 53.08 kPa. The AUC values for the conventional ultrasound, the AI-assisted ultrasound diagnostic system, and the combination of conventional ultrasound and SWE (Emax) were 0.79, 0.82, and 0.81, respectively. The AUC of the AI-assisted ultrasound diagnostic system was higher than that of the SWE (Emax) alone (P＜0.05), and was comparable to that of the combined model of conventional ultrasound and SWE (Emax). Conclusion The AI-assisted ultrasound diagnostic system enables automated assessment of benign and malignant nodules and demonstrates high diagnostic accuracy in differentiating breast nodules. Its diagnostic performance is comparable to that of a combined model of conventional ultrasound and SWE (Emax), indicating promising prospects for intelligent diagnostic applications.