Deng Jie, Qin YuHui
First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China.
Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China.
Ophthalmic Epidemiol. 2025 Jun;32(3):245-258. doi: 10.1080/09286586.2024.2373956. Epub 2024 Aug 15.
Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making.
Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix.
The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others.
The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.
人工智能（AI）在眼科领域已受到广泛关注。本文对该领域的研究文献进行综述、分类和总结，旨在使读者详细了解当前状况和未来发展方向，为进一步研究和决策奠定坚实基础。
从科学网数据库检索文献。使用VOSviewer、CiteSpace和R包Bibliometrix进行文献计量分析。
该研究纳入了来自98个国家4035个机构的3377篇出版物。中国和美国的出版物数量最多。中山大学是领先机构。《转化视觉科学与技术》发表的文章最多，而《眼科学》的共被引次数最多。在13145名研究人员中，丁DSW的出版物和被引次数最多。关键词包括“深度学习”“糖尿病视网膜病变”“机器学习”等。
该研究突出了AI在眼科领域的广阔前景。自动化眼病筛查，尤其是其视网膜图像分割和识别的核心技术，已成为研究热点。AI也在向手术辅助、预测模型等复杂领域拓展。多模态AI、生成对抗网络和ChatGPT推动了进一步的技术创新。然而，在眼科领域实施AI也面临诸多挑战，包括技术、监管和伦理等问题。随着这些挑战的克服，我们期待更多创新应用，为更有效、更安全的眼病治疗铺平道路。
