Diagnostic Performance of Artificial Intelligence in Salivary Gland Tumors Using Ultrasound Imaging: A Systematic Review

Background Salivary gland tumors are heterogeneous, making diagnosis challenging. Artificial intelligence (AI) is a potential adjunct in diagnosis, though its performance in ultrasound-based evaluation of salivary gland tumors is not clear. Methods Publication databases were searched from inception to August 2025. Eligible studies applied AI techniques to ultrasound for salivary gland tumors and reported diagnostic performance against histopathology. Results From 1,239 records, 17 retrospective studies including 5351 patients were eligible. A range of AI methodologies were used. Convolutional neural networks (CNN) were the most applied approach, with additional use of radiomics, ensemble, and hybrid deep learning machine learning models. Diagnostic performance ranged from moderate to excellent across individual studies, with the best-performing models achieving an area under the curve (AUC) of 0.97, while some models showed only modest discrimination (AUC as low as 0.58). Conclusion AI models applied to ultrasound imaging suggest promising diagnostic performance across varied classification tasks for salivary gland tumors. Prospective multicenter studies with standardized protocols are required before clinical implementation.