Gao YingZheng, Chen JiaHao, Fu Tao, Gu Yi, Du WeiDong
The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
Front Oncol. 2025 Mar 18;15:1525650. doi: 10.3389/fonc.2025.1525650. eCollection 2025.
In recent years, with the rapid advancement of computer science, artificial intelligence has found extensive applications and has been the subject of significant research within the healthcare industry, particularly in areas such as medical imaging, diagnostics, biomedical engineering, and health data analytics. Artificial intelligence has also made considerable inroads in the diagnosis and treatment of thyroid cancer. This study aims to evaluate the progress, current hotspots, and potential future directions of research on artificial intelligence in the field of thyroid cancer through a bibliometric analysis. This study retrieved literature on the application of artificial intelligence in thyroid cancer from 2004 to 2024 from the Web of Science Core Collection (WoSCC) database. A retrospective bibliometric analysis and visualization study of the filtered data were conducted using VOSviewer, CiteSpace, and the Bibliometrix package in R software. A total of 956 articles from 70 countries/regions were included. China had the highest number of publications, with Shanghai Jiao Tong University (China) being the most prolific research institution. The most prolific author was Wei, X. (n=14), while Haugen, B. R. was the most co-cited author (n=297). The Frontiers in Oncology (35 articles, IF=3.5, Q1) was the most frequently publishing journal, and Thyroid (cited 1,705 times) was the most co-cited journal. Keywords such as 'ultrasound,' 'deep learning,' and 'diagnosis' indicate research hotspots in this field. This study provides a comprehensive exposition of the current advancements, emerging trends, and future directions of artificial intelligence in thyroid cancer research. It serves as a valuable resource for clinicians and researchers, offering a systematic understanding of key focal areas in the field, thereby assisting in the identification and determination of future research trajectories.
近年来，随着计算机科学的迅速发展，人工智能得到了广泛应用，并成为医疗行业大量研究的主题，尤其是在医学成像、诊断、生物医学工程和健康数据分析等领域。人工智能在甲状腺癌的诊断和治疗方面也取得了显著进展。本研究旨在通过文献计量分析评估人工智能在甲状腺癌领域的研究进展、当前热点以及未来潜在的研究方向。本研究从科学网核心合集（WoSCC）数据库中检索了2004年至2024年关于人工智能在甲状腺癌中应用的文献。使用VOSviewer、CiteSpace和R软件中的Bibliometrix包对筛选后的数据进行了回顾性文献计量分析和可视化研究。共纳入了来自70个国家/地区的956篇文章。中国的出版物数量最多，上海交通大学（中国）是发文量最高的研究机构。发文量最高的作者是Wei, X.（n = 14），而Haugen, B. R.是被引频次最高的作者（n = 297）。《肿瘤前沿》（35篇文章，IF = 3.5，Q1）是发文最频繁的期刊，《甲状腺》（被引1705次）是被引频次最高的期刊。“超声”“深度学习”和“诊断”等关键词表明了该领域的研究热点。本研究全面阐述了人工智能在甲状腺癌研究中的当前进展、新趋势和未来方向。它为临床医生和研究人员提供了宝贵的资源，有助于系统地了解该领域的关键重点领域，从而辅助确定未来的研究轨迹。
