Systematic review reports improved diagnostic performance of AI models for cervical lymph node metastasis in papillary thyroid carcinoma, suggesting potential for clinical use.
Purpose This systematic review and meta‐analysis evaluated the performance of imaging‐based artificial intelligence (AI) models in diagnosing preoperative cervical lymph node metastasis (LNM) in clinically node‐negative (cN0) papillary thyroid carcinoma (PTC). Methods We conducted a literature search in PubMed, Embase, and Web of Science until February 25, 2025. Studies were selected that focused on imaging‐based AI models for predicting cervical LNM in cN0 PTC. The diagnostic performance metrics were analyzed using a bivariate random‐effects model, and study quality was assessed with the QUADAS‐2 tool. Results From 671 articles, 11 studies involving 3366 patients were included. Ultrasound (US)‐based AI models showed pooled sensitivity of 0.79 and specificity of 0.82, significantly higher than radiologists ( p < 0.001). CT‐based AI models demonstrated sensitivity of 0.78 and specificity of 0.89. Conclusions Imaging‐based AI models, particularly US‐based AI, show promising diagnostic performance. There is a need for further multicenter prospective studies for validation.
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