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Background: Risk stratification systems for thyroid nodules are limited by low specificity. The fine-needle aspiration (FNA) biopsy size thresholds and stratification criteria are based on evidence from the literature and expert consensus. Our aims were to investigate the optimal FNA biopsy size thresholds in the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and artificial intelligence (AI) TI-RADS and to revise the stratification criteria in AI TI-RADS. Methods: A total of 2596 thyroid nodules (in 2511 patients) on ultrasound examination with definite pathological diagnoses were retrospectively identified from January 2017 to September 2021 in 6 participating Chinese hospitals. The modified criteria for ACR TI-RADS were as follows: (1) no FNA for TR3; (2) FNA threshold for TR4 increased to 2.5 cm. The modified criteria for AI TI-RADS were as follows: (1) 6-point nodules upgraded to TR5; (2) no FNA for TR3; (3) FNA threshold for TR4 increased to 2.5 cm. The diagnostic performance and the unnecessary FNA rate (UFR) of modified versions were compared with the original ACR TI-RADS. Results: Compared with the original ACR TI-RADS, the modified ACR (mACR) TI-RADS yielded higher specificity (73% vs. 46%), accuracy (74% vs. 51%), area under the receiver operating characteristic curve (AUC; 0.80 vs. 0.70), and lower UFR (25% vs. 48%; all p  < 0.001), although the sensitivity was slightly decreased (87% vs. 93%, p  = 0.057). Compared with the original ACR TI-RADS, the modified AI (mAI) TI-RADS yielded higher specificity (73% vs. 46%), accuracy (75% vs. 51%), AUC (0.81 vs. 0.70), and lower UFR (24% vs. 48%; all p  < 0.001), although the sensitivity tended to be slightly decreased (89% vs. 93%, p  = 0.13). There was no significant difference between the mACR TI-RADS and mAI TI-RADS in the diagnostic performance and UFR (all p  > 0.05). Conclusions: The revised FNA thresholds and the stratification criteria of the mACR TI-RADS and mAI TI-RADS may be associated with improvements in specificity and accuracy, without significantly sacrificing sensitivity for malignancy detection.