Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature
Abstract
Purpose: This study aims to generate and elucidate the latest perspectives on the application of artificial intelligence (AI) in ophthalmology using bibliometric methods. By analyzing literature from the past 5 years (2020-2024), we seek to outline the development trends of this technology, provide guidance for its future directions, and assist clinicians in adapting to these innovations.
Methods: We conducted a comprehensive search of all literature related to AI and ophthalmology in the Web of Science Core Collection (WoSCC) using bibliometric methods. The collected data were analyzed and visualized using three widely recognized bibliometric software tools: CiteSpace, VOSviewer, and the R package "Bibliometrix."
Results: A total of 21,725 documents were included from 134 countries and 7,126 institutions, consisting of 19,978 articles (91.96%) and 1,714 reviews (8.04%), with China and the United States leading the contributions. The number of publications in AI and ophthalmology has increased annually, with the University of California System, the National University of Singapore, and the University of London being the primary research institutions. Ophthalmology and Proc CVPR IEEE are the most co-cited journals and conferences in this field. These papers were authored by 87,695 individuals, with Wang Y, Liu Y, and Zhang Y the most prolific authors. Ting DSW was the most co-cited author. Major research topics include using various models to scan retinal images for…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 87,695 participants or records.
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