ObjectiveTo understand the research status, academic hotspots, and development trends in the interdisciplinary field of fundus diseases and artificial intelligence (AI). MethodsThe SCI-Expanded database from the Web of Science core collection, provided by the Institute for Scientific Information in the United States, was used as the data source to retrieve literature related to the intersection of retinal diseases and AI from January 1, 2014, to December 31, 2024. Bibliometric analysis tools, Origin Pro 2024 and CiteSpace 6.4.R1, were employed to analyze data on countries/regions, institutions, journals, authors, and keywords. ResultsA total of 2 103 papers related to the intersection of retinal diseases and AI were identified. The number of publications increased significantly starting in 2018, with an average annual increase of approximately 56.8 papers. Among the countries/regions that published papers, China had the highest number of publications (587), while the United Kingdom exhibited the highest intermediary centrality, with a value of 0.28. The core journal in this field was Ophthalmology, with an impact factor of 13.2. With respect to authors, the Ophthalmic Hospital of the University of Vienna in Austria had the highest number of publications (50). Keyword clustering revealed that research efforts focused on three main areas: AI-assisted diagnosis of retinal diseases (#1, #3-#5, #7), analysis of retinal images (#0, #6, #8, #9), and practical applications of retinal disease research (#2). The most frequently occurring keywords were "diabetic retinopathy", "deep learning”, and "AI", with 984, 749, and 471 occurrences, respectively. The analysis of emergent words shows that "retinal image" and "risk factor" are the persistent hotspots, while the field of mathematics is the key technical support. ConclusionsFrom 2014 to 2024, there was a growing trend in literature related to the intersection of retinal diseases and AI. China had the highest number of publications, but its intermediary centrality was relatively low. Research activities focused primarily on AI-assisted diagnosis, image analysis, and practical applications of retinal disease knowledge.
