Xu Mianmian, Chen Yafang, Wu Tianen, Chen Yuyan, Zhuang Wanling, Huang Yinhui, Chen Chuanzhen
Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, China.
Department of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Front Oncol. 2025 Jan 7;14:1456144. doi: 10.3389/fonc.2024.1456144. eCollection 2024.
To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.
The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences.
A total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were "model" and "human papillomavirus." The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates.
By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.
运用文献计量学方法分析1994年至2024年人工智能在肿瘤护理领域的研究前景与发展趋势，为肿瘤护理专业人员及研究人员提供指导与参考。
检索Web of Science数据库核心合集1994年至2024年的相关文章。使用R包“Bibliometrix”分析主要文献计量特征，绘制三域图以展示机构、国家和关键词之间的关系。VOSviewer用于合作作者分析，其可视化功能用于共现分析。CiteSpace计算引文突发和关键词出现情况。
共检索到517篇文章，涉及80个国家/地区。美国发表文章数量最多，为188篇（36.4%），其次是中国，有79篇（15.3%）。发表量排名前十的机构均为美国的大学或癌症研究所，哈佛大学位居榜首。Repici、Aerts和Almangush等知名研究团队在人工智能用于肿瘤危险因素识别和症状管理的研究方面做出了重大贡献。近年来，突发强度最高的关键词是“模型”和“人乳头瘤病毒”。研究最多的肿瘤类型是乳腺癌。虽然《Cancers》发表的文章数量最多，但《CA：临床医师癌症杂志》和《PLOS ONE》等期刊的影响力和引用率更高。
通过分析肿瘤护理领域人工智能文献的数量，结合对机构、核心作者、期刊和关键词的统计分析，揭示了过去30年人工智能在肿瘤护理领域应用的研究热点和趋势。肿瘤护理领域的人工智能正进入快速发展阶段，为该领域的学者和专业人员提供了有价值的参考。
