Objective To investigate the application value of artificial intelligence (AI) system AI-SONIC^TM Thyroid 5.3.3.0 based on ultrasound image analysis in the risk assessment of malignant thyroid nodules. Methods A total of 573 thyroid nodules were selected from 453 patients who were admitted to The Second Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2019 to Jan. 2021. With the postoperative pathology as the gold standard, the differential diagnostic efficacies of preoperative AI system examination for benign and malignant thyroid nodules in different gender groups, different age groups, and different nodule size groups were evaluated by χ² test and receiver operating characteristic (ROC) curve. The efficacies of preoperative AI system examination and conventional ultrasonography by different seniority ultrasound physicians before operation in diagnosing benign and malignant thyroid nodules were compared by DeLong test. Results Of the 573 thyroid nodules examined before operation, 411 (76.5%) were malignant and 162 (23.5%) were benign as confirmed by pathology after operation. The sensitivity, specificity, and accuracy of conventional ultrasonography in diagnosing benign and malignant thyroid nodules were 85.2% (350/411), 55.6% (90/162), and 76.8% (440/573), respectively, with an area under curve (AUC) of 0.721 (95% confidence interval [CI] 0.672-0.771) in the junior ultrasound physicians; the sensitivity, specificity, and accuracy were 93.9% (386/411), 74.1% (120/162), and 88.3% (506/573), respectively, with an AUC of 0.865 (95% CI 0.825-0.904) in the senior ultrasound physicians. The sensitivity, specificity, and accuracy of the AI system were 92.5% (380/411), 69.1% (112/162), and 85.9% (492/573), respectively, with an AUC of 0.809 (95% CI 0.764-0.854). DeLong test results showed that the AUC of the AI system in diagnosing benign and malignant thyroid nodules was significantly higher than that of the junior ultrasound physicians (P=0.032), and there was no significant difference between the AI system and senior ultrasound physicians (P>0.05). There was no significant difference in the accuracy of the AI system in diagnosing benign or malignant thyroid nodules among patients with different genders or different ages (P>0.05). For nodules of different sizes, when the maximum diameter of nodules was 10-<15 mm, the AUC of the AI system was the highest, being 0.882 (95% CI 0.723-0.916). Conclusion AI-SONIC^TM Thyroid 5.3.3.0 can identify benign and malignant features of thyroid nodules, and its diagnostic efficiency is close to that of senior ultrasound physicians. It is expected to be a practical tool to predict the risk of malignant thyroid nodules before clinical operation.