人工智能在眼科领域的应用现状、热点和前景:文献计量分析(2003-2023 年)》。

IF 1.7 4区 医学 Q3 OPHTHALMOLOGY
Jie Deng, YuHui Qin
{"title":"人工智能在眼科领域的应用现状、热点和前景:文献计量分析(2003-2023 年)》。","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":"{\"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}","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

摘要

目的:人工智能(AI)在眼科领域受到了广泛关注。本文对该领域的研究文献进行了回顾、分类和总结,旨在让读者详细了解该领域的现状和未来发展方向,为进一步的研究和决策奠定坚实的基础:方法:从 Web of Science 数据库中检索文献。方法:从 Web Science 数据库中检索文献,使用 VOSviewer、CiteSpace 和 R 软件包 Bibliometrix 进行文献计量分析:研究包括来自 98 个国家 4035 个机构的 3,377 篇出版物。中国和美国的出版物最多。中山大学在这方面处于领先地位。Translational Vision Science & Technology "发表的文章最多,而 "Ophthalmology "被联合引用的次数最多。在 13145 名研究人员中,丁大伟发表的论文和被引用的次数最多。关键词包括 "深度学习"、"糖尿病视网膜病变"、"机器学习 "等:这项研究凸显了人工智能在眼科领域的广阔前景。自动眼病筛查,尤其是其核心技术--视网膜图像分割和识别,已成为研究热点。人工智能还在向手术辅助、预测模型等复杂领域扩展。多模态人工智能、生成式对抗网络和 ChatGPT 推动了进一步的技术创新。然而,在眼科领域实施人工智能还面临着许多挑战,包括技术、监管和伦理问题等。随着这些挑战的克服,我们预计会有更多的创新应用,为更有效、更安全的眼科疾病治疗铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023).

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ophthalmic epidemiology
Ophthalmic epidemiology 医学-眼科学
CiteScore
3.70
自引率
5.60%
发文量
61
审稿时长
6-12 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信