Abstract:

Objective To analyze the research hotspots and development trends of artificial intelligencein the field of organ transplantation. Methods Literatures on the application of artificial intelligence inthe field of organ transplantation from January 2004 to June 2024 were retrieved from the Web of ScienceCore Collection and China National Knowledge Infrastructure database. CiteSpace 6.2.R6 software was used for visual analysis. Results A total of 728 relevant literatures were retrieved，559 English literatures and 143 Chinese literatures were included after screening. The number of literatures published in the early stage was small，while the number of literatures published in recent years has increased significantly. The country with the largest number of articles was the United States，followed by China. The top three institutions in terms of foreignpublications were Mayo Clinic，University of Toronto and Harvard University. Zhejiang University，Peking UnionMedical College and the First Affiliated Hospital of Sun Yat-sen University were the most active institutions in China. The authors who have published the most articles in English were Cheungpasitporn Wisit and Thongprayoon Charat，and the author who has published the most articles in Chinese was Ren Bin. The main high-frequency keywords in English were machine learning，survival，artificial intelligence，liver transplantation，kidney transplantation，mortality，risk，model，outcome and deep learning. The main high-frequency keywords in Chinese were machine learning， liver transplantation， kidney transplantation， deep learning， artificial intelligence， hepatocellular carcinoma，radiomics，liver cancer，prognosis and tacrolimus. The first emergent keywords in English were artificial neural networks，data mining and survival analysis，and the most recent were predictive model and kidney transplant. The first emergent keywords in Chinese were neural networks，cyclosporine A，and blood concentration，and the most recent were deep learning and machine learning. Conclusion The application of artificial intelligence in thefield of organ transplantation is increasing significantly. The use of machine learning and deep learning to construct prediction models and to analyze the survival of various organ transplantation patients is a current research hotspots. In the future，exchanges and cooperation between countries and disciplines can be strengthened to promote the study and application of advanced AI technologies，so as to further promote the development of this field. 

Key words:

"> Organ transplantation, Artificial intelligence, Deep learning, Machine learning, Large language model, Research hotspots, Visual analysis, Bibliometrics

摘要：

目的 分析人工智能在国内外器官移植领域的研究热点和发展趋势。 方法 检索 Web of Science 核心合集数据库和中国知网数据库收录的有关人工智能在器官移植领域应用的相关文献，检索时限为 2004 年 1 月至 2024 年 6 月，运用 CiteSpace 6.2.R6 软件进行可视化分析。 结果 共检索到 728 篇相关文献，经筛选后纳入 559 篇英文文献和 143 篇中文文献，前期发文量较少，近几年发文量有明显上升趋势。英文发文量最多的国家是美国，其次为中国。国外发文量排前三的机构分别是梅奥诊所、多伦多大学和哈佛大学。国内发文最活跃的机构是浙江大学、北京协和医学院和中山大学附属第一医院。英文发文量最多的作者是Cheungpasitporn Wisit 和 Thongprayoon Charat，中文发文量最多的作者是任斌。主要的英文高频关键词是机器学习、生存、人工智能、肝移植、肾移植、死亡率、风险、模型、结局和深度学习，主要的中文高频关键词是机器学习、肝移植、肾移植、深度学习、人工智能、肝细胞癌、影像组学、肝癌、预后和他克莫司。英文中最早出现的突现关键词是人工神经网络、数据挖掘和生存分析，最近出现的是预测模型和肾移植。中文中最早出现的突现关键词是神经网络、环孢素 A 和血药浓度，最近出现的是深度学习和机器学习。 结论 人工智能在器官移植领域的应用研究正显著增加，使用机器学习和深度学习对各种器官移植受者进行预测模型构建和生存分析是目前的研究热点。未来可加强国家间、学科间的交流合作，推进先进人工智能技术的学习和应用，以进一步促进该领域的发展。

关键词:

器官移植 , 人工智能 , 深度学习 , 机器学习 , 大语言模型 , 研究热点 , 可视化分析 , 文献计量学