- Preoperative differentiation between benign and malignant parotid tumors is challenging but can influence treatment and outcome. In this proof-of-concept study, we investigated multimodal AI-based classification combining clinical tabular data and ultrasound images, comparing unimodal and multimodal approaches for binary benign–malignant classification. The study included 594 patients with parotid tumors (449 benign, 145 malignant) after preprocessing. The image-only classifier achieved 0.73 accuracy at image level and 0.75 after patient-level aggregation. The tabular model reached 0.88 accuracy, while the multimodal model maintained this accuracy with a slightly improved weighted F1 score (0.89) and malignant recall of 0.93. These results suggest that image-derived features provided complementary information, primarily improving sensitivity for malignant tumors.
