Supervisor：China Association for Science and TechnologySponsor：Chinese Medical Association Editor-in-chief：Li Xiaoxin Publishing period：
Monthly
ISSN：1005-1015CN：51-1434/R
Objective To understand the research status, academic hotspots, and development trends in the interdisciplinary field of fundus diseases and artificial intelligence (AI). Methods The 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. Results A 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. Conclusions From 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.
Citation：Yu Man, Xu Hao, Yang Chun, Tan Anzu. Bibliometric analysis of artificial intelligence applications in fundus diseases research from 2014 to 2024. Chinese Journal of Ocular Fundus Diseases, 2026, 42(2): 155-162. doi: 10.3760/cma.j.cn511434-20250508-00210Copy
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2. Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis[J/OL]. Lancet Glob Health, 2014, 2(2): e106-e116[2014-01-03]. https://pubmed.ncbi.nlm.nih.gov/25104651/. DOI: 10.1016/S2214-109X(13)70145-1.
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5. 王崇德. 文獻計量學引論[M]. 桂林: 廣西師范大學出版社, 1997: 29-31.Wang CD. Introduction to bibliometrics[M]. Guilin: Guangxi Normal University Press, 1997: 29-31.
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10. Kim KM, Heo TY, Kim A, et al. Development of a fundus image-based deep learning diagnostic tool for various retinal diseases[J/OL]. J Pers Med, 2021, 11(5): 321[2021-04-21]. https://pubmed.ncbi.nlm.nih.gov/33918998/. DOI: 10.3390/jpm11050321.
11. Wang M, Zhang X, Li D, et al. The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study[J/OL]. BMC Med Educ, 2025, 25(1): 503[2025-04-08]. https://pubmed.ncbi.nlm.nih.gov/40200344/. DOI: 10.1186/s12909-025-07066-1.
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15. Zhang J, Lin S, Cheng T, et al. RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis[J/OL]. NPJ Digit Med, 2024, 7(1): 108[2024-04-30]. https://pubmed.ncbi.nlm.nih.gov/38693205/. DOI: 10.1038/s41746-024-01109-5.
16. Zhao J, Lu Y, Zhu S, et al. Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in ophthalmic disease diagnosis[J/OL]. Front Pharmacol, 2022, 13: 930520[2022-06-08]. https://pubmed.ncbi.nlm.nih.gov/35754490/. DOI: 10.3389/fphar.2022.930520.
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19. Jin K, Yan Y, Chen M, et al. Multimodal deep learning with feature level fusion for identification of choroidalneovascularization activity in age-related macular degeneration[J/OL]. Acta Ophthalmol, 2022, 100(2): e512-e520[2021-06-23]. https://pubmed.ncbi.nlm.nih.gov/34159761/. DOI: 10.1111/aos.14928.
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25. Reiter GS, Schmidt-Erfurth U. Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy[J/OL]. Ther Adv Ophthalmol, 2022, 14: 25158414221083363[2022-03-23]. https://pubmed.ncbi.nlm.nih.gov/35340749/. DOI: 10.1177/25158414221083363.
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Citation：Yu Man, Xu Hao, Yang Chun, Tan Anzu. Bibliometric analysis of artificial intelligence applications in fundus diseases research from 2014 to 2024. Chinese Journal of Ocular Fundus Diseases, 2026, 42(2): 155-162. doi: 10.3760/cma.j.cn511434-20250508-00210
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