{"title":"Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023).","authors":"Jie Deng, YuHui Qin","doi":"10.1080/09286586.2024.2373956","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":19607,"journal":{"name":"Ophthalmic epidemiology","volume":" ","pages":"1-14"},"PeriodicalIF":1.7000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ophthalmic epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/09286586.2024.2373956","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: 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.
Methods: Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix.
Results: 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.
Conclusion: 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.
期刊介绍:
Ophthalmic Epidemiology is dedicated to the publication of original research into eye and vision health in the fields of epidemiology, public health and the prevention of blindness. Ophthalmic Epidemiology publishes editorials, original research reports, systematic reviews and meta-analysis articles, brief communications and letters to the editor on all subjects related to ophthalmic epidemiology. A broad range of topics is suitable, such as: evaluating the risk of ocular diseases, general and specific study designs, screening program implementation and evaluation, eye health care access, delivery and outcomes, therapeutic efficacy or effectiveness, disease prognosis and quality of life, cost-benefit analysis, biostatistical theory and risk factor analysis. We are looking to expand our engagement with reports of international interest, including those regarding problems affecting developing countries, although reports from all over the world potentially are suitable. Clinical case reports, small case series (not enough for a cohort analysis) articles and animal research reports are not appropriate for this journal.