Background: Accurate interpretation of thyroid ultrasound requires substantial experience, and diagnostic performance among trainees remains variable. Artificial intelligence (AI) has shown promise in medical image analysis; however, its role as an educational support tool for trainees has not been fully established. This study aimed to evaluate whether an AI-assisted system could improve trainee performance in thyroid ultrasound interpretation.Methods: We developed an AI-based diagnostic support system using a Mask R-CNN framework trained on pathology-confirmed thyroid ultrasound images. The model performed segmentation and classification of five lesion categories. A reader study was conducted involving five expert physicians and five trainees. Trainees evaluated ultrasound images under non-assisted and AI-assisted conditions. Diagnostic performance was assessed using sensitivity, specificity, precision, and F1 score.Results: The AI model demonstrated high segmentation accuracy for several structures, including vessels and thyroid parenchyma. However, its ability to distinguish benign from malignant tumors remained limited. Despite this, AI assistance improved trainee performance. Median precision increased from 0.53 to 0.63, and F1 score improved from 0.53 to 0.61. Specificity increased from 0.22 to 0.50, while sensitivity remained comparable. These findings indicate that AI support primarily reduced false-positive interpretations and improved diagnostic consistency among trainees.Conclusions: AI-assisted ultrasound interpretation improved the diagnostic performance of trainees, particularly by enhancing precision and reducing false-positive findings. Although the AI model alone was insufficient for independent clinical use, it demonstrated potential as an educational support tool. These findings suggest that AI may contribute to more consistent and effective training in thyroid ultrasound interpretation.