人工智能（AI）系统可以用类似或更好的性能诊断甲状腺结节，超过放射科医生的表现。关于这种性能与细针穿刺（FNA）实现的性能如何相比，目前知之甚少。本研究旨在比较FNA细胞病理学单独与结合BRAFV600E突变分析和AI诊断系统的诊断效果。我们收集了三家医院的637个甲状腺结节的超声图像。根据与两次半年间隔的组合FNA和突变分析检查的切后病理学和持续良性结局相一致的金标准，以敏感性、特异性、准确性和κ系数评估了AI诊断系统、基于FNA细胞病理学和BRAFV600E突变分析的诊断效果。AI系统的恶性阈值是根据346个结节的回顾性队列使用Youden指数选择的，并应用于291个结节的前瞻性队列。FNA细胞病理学与根据Bethesda标准的BRAFV600E突变分析的组合在我们的多中心研究中，无论是在任何队列中，其准确性均与AI系统无显著差异。此外，对于45个包括不确定Bethesda III和IV类的结节，AI系统的准确性、敏感性和特异性分别达到84.44%、95.45%和73.91%。AI诊断系统显示了与FNA细胞病理学结合BRAFV600E突变分析相似的诊断性能。鉴于其操作性、时间效率、非侵入性和超声广泛可用性的优点，它为甲状腺结节诊断提供了一种新的选择。甲状腺超声人工智能对甲状腺结节的诊断具有与FNA细胞病理学结合BRAFV600E突变分析相当的统计学性能。它可以在医院和诊所广泛应用，辅助放射科医生进行甲状腺结节筛查，并有望减少相对侵入性的FNA活检的需求。 • 在回顾性队列的346个结节中，评估的人工智能（AI）系统的准确性与单独的细针穿刺（FNA）细胞病理学以及结合基因突变分析并无显著差异。 • 在前瞻性多中心队列的291个结节中，AI诊断系统的准确性与FNA细胞病理学，在单独或结合基因突变分析方面，均无显著差异。 • 对于45个不确定的Bethesda III和IV类结节，AI系统的表现与BRAFV600E突变分析无显著差异。 © 2023. 许可有限制的作者，欧洲放射学会独家许可使用。

Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system.The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval.The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively.The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis.Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies.• In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.© 2023. The Author(s), under exclusive licence to European Society of Radiology.