Artificial intelligence in preoperative diagnosis of thyroid nodules: a bibliometric analysis

Objectives To explore the current research progress and trends in the application of artificial intelligence (AI) for preoperative diagnosis of thyroid nodules (TN), and to identify the key research directions through bibliometric analysis. Methods Articles on the application of AI for preoperative diagnosis of TN were retrieved from the Web of Science core collection and Scopus databases. Tools like VOSviewer, CiteSpace, the bibliometrix, Scimago Graphica, and Charticulator were used for bibliometric analysis. Trends in annual publishing volumes, collaborations between authors and institutions, highly cited articles, keyword co-occurrence, keyword clustering, and keyword burst analysis are all included. Results A total of 1370 publications were included in this study, with an rapid growth trend observed in the annual publications in this field. China has made significant contributions, with Shanghai Jiao Tong University producing the most publications. Chinese scholar XuDong emerged as the most prolific author (30 publications). Journal analysis revealed that Thyroid is the highest-impact journal in terms of citation frequency. High-frequency keywords in the field include “TN”, “cancer” and “ultrasound”, while recent emerging keywords include “semantic segmentation”, and “AI”. Conclusions Since 2019, the application of AI in the preoperative diagnosis of TN has attracted increasing research attention, with China emerging as a major contributor to this research field. The number of journals publishing in this domain has significantly increased, and the publications reflect a trend toward interdisciplinary research. The research focus has gradually shifted from conventional diagnostic approaches toward pixel-level lesion quantification, fine-grained feature extraction and the integration of AI-enabled diagnostic approaches, highlighting the potential of AI to improve TN assessment.