Health AI Central
Peer-Reviewed Publication
Cancer Cytopathol2026;134(10):e70149.October 1, 2026Journal Article

Artificial intelligence-assisted digital thyroid FNA cytology: Improved agreement and sensitivity for higher-risk Bethesda categories with enhanced screening efficiency.

Swati Satturwar1, Zaibo Li1, Chi-Shun Yang2, Yi-Jyun Lin3, Wei-Lei Yang4, Ming-Yu Lin4, Cheng-Hung Yeh4, Shih-Wen Hsu4, Yi-Siou Liu4, Guowei Shao4, Tien-Jen Liu4, Chih-Jung Chen2,5,6, Barbara A Crothers4
1Department of Pathology, The Ohio State University, Columbus, Ohio, USA.
2Department of Pathology and Laboratory Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
3Independent Medical Laboratory Consultant, Taipei, Taiwan.
4AIxMed, Inc., Santa Clara, California, USA.
5School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
6Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
PubMed DOI

Abstract

BACKGROUND: Accurate cytologic classification of thyroid nodules is essential for clinical management, but interobserver variability and indeterminate interpretations remain persistent challenges. The clinical feasibility of AIxTHY, a disease-specific deep-learning algorithm integrated into a digital cytology platform, was evaluated for assisting thyroid fine-needle aspiration (FNA) diagnosis usin…

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