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To address this, AI\u2010TFNA is developed, a robust artificial intelligence platform leveraging extensive clinical data to enhance diagnostic accuracy and clinical efficiency. A total of 20,803 thyroid samples are collected from seven medical centers across different regions in China. Of these, 4,421 thyroid fine\u2010needle aspiration (TFNA) samples from three hospitals are used to train AI\u2010TFNA, ensuring strong generalizability across diverse clinical settings. For the internal validation, AI\u2010TFNA demonstrates exceptional performance: the overall accuracy of TBS I is 93.27%, the sensitivity of TBS V and TBS VI reaches 85.37% and 83.78%, while the specificity of TBS II is 97.13%. Consistent results are observed in an external cohort of 2,153 samples, demonstrating robust generalizability. The incorporation of BRAF mutation data into AI\u2010TFNA and the development of a multi\u2010modal model further improve precision by significantly improving the differentiation between benign and malignant thyroid nodules. Image Appearance Migration (IAM) is an innovative technique that substantially improves cross\u2010institutional model generalizability, increasing AI\u2010TFNA sensitivity by 1.90% and specificity by 8.12%. AI\u2010TFNA offers rapid, reliable decision support, advancing thyroid nodule diagnostics.<\/jats:p>","DOI":"10.1002\/advs.202511369","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:51:14Z","timestamp":1760601074000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning"],"prefix":"10.1002","volume":"12","author":[{"given":"Yuanzheng","family":"Lou","sequence":"first","affiliation":[{"name":"Department of Pathology Nanfang Hospital and School of Basic Medical Sciences Southern Medical University  Guangzhou 510515 China"},{"name":"College of Bioengineering Chongqing University  Chongqing 400030 China"},{"name":"Institute of Pathology &amp; Southwest Cancer Center the First Affiliated Hospital (Southwest Hospital) and School of Basic Medical Sciences Army Medical University (Third Military Medical University), and the Key Laboratory of Tumor Immunopathology the Ministry of Education (Third Military Medical University)  Chongqing 400038 China"},{"name":"Chongqing Institute of Advanced Pathology Jinfeng Laboratory  Chongqing 400041 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjian","family":"Su","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Advanced Pathology Jinfeng Laboratory  Chongqing 400041 China"},{"name":"Guangzhou F.Q. 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