目的：基于文献计量学和高影响力论文研究糖尿病视网膜病变人工智能研究的热点和趋势。

方法：检索2012-01-01/2022-12-31在Web of Science Core Collection(WoSCC)发表的关于糖尿病视网膜病变人工智能研究的论文，使用CiteSpace软件分析年发文量、国家、机构、论文来源、研究领域、关键词等，并进一步分析高影响力论文。

结果：纳入79个国家关于糖尿病视网膜病变人工智能研究的论文1 009篇，其中2022年发文量272篇； 中国和印度发文量分别为287、234篇。英国的中心性为0.31，美国的H指数为48，英国的3家机构(伦敦大学、莫菲尔德眼科医院、伦敦大学学院)和埃及的1家机构(埃及知识库)H指数均达到14。该研究领域涉及的主要学科为眼科学、计算机科学和人工智能，2021～2022年突现关键词是迁移学习、血管分割和卷积神经网络。

结论：中国在这一领域发文量最大，美国被认为是该领域的领先国家，埃及知识库和伦敦大学为该领域的领先机构，IEEE Access为该领域最活跃的期刊。糖尿病视网膜病变人工智能研究领域的研究重点已经从人工智能用于疾病检测和分级以辅助诊断转向对其辅助诊断系统的研究，迁移学习、血管分割和卷积神经网络在该领域具有广泛的应用前景。

METHODS: Papers on artificial intelligence in DR research published in the Web of Science Core Collection(WoSCC)from January 1, 2012, to December 31, 2022 were retrieved. The data was analyzed by CiteSpace software to examine annual publication number, countries, institutions, source journal, research categories, keywords, and to perform an in-depth analysis of high-impact papers.

RESULTS: A total of 1 009 papers on artificial intelligence in DR from 79 countries were included in the study, with 272 papers published in 2022. Notably, China and India contributed 287 and 234 papers, respectively. The United Kingdom exhibited a centrality score of 0.31, while the United States boasted an impressive H-index of 48. Three prominent institutions in the United Kingdom(University of London, Moorfields Eye Hospital, and University College London)and one institution in Egypt(Egyptian Knowledge Bank)all achieved a notable H-index of 14. The primary academic disciplines associated with this research field encompassed ophthalmology, computer science, and artificial intelligence. Burst keywords in the years 2021～2022 included transfer learning, vessel segmentation, and convolutional neural networks.

CONCLUSION: China emerged as the leading contributor in terms of publication number in this field, while the United States stood out as a key player. Notably, Egyptian Knowledge Bank and University of London assumed leading roles among research institutions. Additionally, IEEE Access was identified as the most active journal within this domain. The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Transfer learning, vessel segmentation, and convolutional neural networks hold substantial promise for widespread applications in this field.