Visualization analysis of artificial intelligence in thyroid disease research at home and abroad based on CiteSpace

Corresponding author: Shi Ziwen, shiziwen1990@163.com

DOI: 10.12201/bmr.202601.00084

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  Abstract: Objective To analyze the research trends, collaboration networks, and evolution of hotspots in the field of artificial intelligence (AI) applied to thyroid diseases over the past decade.?Methods?Relevant literature published from January 2015 to December 2025 was retrieved from databases including China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, VIP, and Web of Science Core Collection. The visualization of publication volume, author collaboration, institutional distribution, and keywords was conducted using CiteSpace 6.4.R1 software.?A national cooperation network analysis was conducted using a bibliometric online analysis platform.Results?A total of 1,741 publications were included (742 in Chinese and 999 in English). After 2019, the publication volume increased rapidly, with China leading in terms of research output. Collaboration networks both domestically and internationally were relatively sparse, and domestic research was primarily characterized by intra-institutional cooperation. Research hotspots in China focused on the application of deep learning combined with ultrasound imaging for the benign-malignant differentiation of thyroid nodules, while international research emphasized ultrasound imaging and machine learning-driven diagnosis of thyroid cancer and its integration with molecular diagnostics. Burst analysis showed that recent domestic research has increasingly focused on technologies such as transfer learning and elastography, whereas international research continues to strengthen classification accuracy and computer-aided diagnosis.?Conclusion?AI has advanced rapidly in the field of thyroid diseases, with distinct emphases in domestic and international research. Future efforts should enhance cross-institutional collaboration, improve the construction of high-quality datasets, and advance clinical translation and validation.

  Key words: Artificial Intelligence; Thyroid Diseases; Bibliometrics; Visualization Analysis; CiteSpace

  Submit time: 29 January 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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