神经眼科中的人工智能:当前实践与未来机遇》。

IF 2 4区 医学 Q3 CLINICAL NEUROLOGY Journal of Neuro-Ophthalmology Pub Date : 2024-09-01 Epub Date: 2024-07-05 DOI:10.1097/WNO.0000000000002205
Rachel C Kenney, Tim W Requarth, Alani I Jack, Sara W Hyman, Steven L Galetta, Scott N Grossman
{"title":"神经眼科中的人工智能:当前实践与未来机遇》。","authors":"Rachel C Kenney, Tim W Requarth, Alani I Jack, Sara W Hyman, Steven L Galetta, Scott N Grossman","doi":"10.1097/WNO.0000000000002205","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.</p><p><strong>Evidence acquisition: </strong>Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.</p><p><strong>Results: </strong>This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.</p><p><strong>Conclusions: </strong>We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.</p>","PeriodicalId":16485,"journal":{"name":"Journal of Neuro-Ophthalmology","volume":" ","pages":"308-318"},"PeriodicalIF":2.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.\",\"authors\":\"Rachel C Kenney, Tim W Requarth, Alani I Jack, Sara W Hyman, Steven L Galetta, Scott N Grossman\",\"doi\":\"10.1097/WNO.0000000000002205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.</p><p><strong>Evidence acquisition: </strong>Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.</p><p><strong>Results: </strong>This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.</p><p><strong>Conclusions: </strong>We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.</p>\",\"PeriodicalId\":16485,\"journal\":{\"name\":\"Journal of Neuro-Ophthalmology\",\"volume\":\" \",\"pages\":\"308-318\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuro-Ophthalmology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/WNO.0000000000002205\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuro-Ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/WNO.0000000000002205","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/5 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:神经眼科经常需要复杂和多方面的临床评估,并辅以精密的成像技术,以评估疾病状况。目前的诊断方法需要大量的专业知识和时间。人工智能的出现为简化和加强这一诊断过程带来了创新解决方案,在神经眼科医生短缺的情况下,这一点尤为重要。特别是机器学习算法,在解释成像数据、识别微妙模式、帮助临床医生做出更准确和及时的诊断以及补充神经眼科疾病的非专业评估方面已显示出巨大的潜力:使用 PubMed 和 Google Scholar 对已发表的文献进行电子检索。在《神经眼科杂志》中对以下术语进行了全面搜索:人工智能、人工智能、机器学习、深度学习、自然语言处理、计算机视觉、大型语言模型和生成式人工智能:本综述旨在全面概述人工智能在神经眼科学中不断发展的应用前景。它将深入探讨人工智能、光学相干断层扫描(OCT)和眼底摄影在开发疾病进展预测模型方面的各种应用。此外,本综述还将探讨如何将生成式人工智能融入神经眼科教育和临床实践:我们回顾了人工智能在神经眼科领域的现状及其潜在的变革性影响。将人工智能纳入神经眼科实践和研究,不仅有望提高诊断准确性,还能开辟新的治疗干预途径。我们强调了人工智能在改善稀缺亚专科资源获取方面的潜力,同时也探讨了目前将人工智能融入临床实践和研究方面所面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.

Background: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.

Evidence acquisition: Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.

Results: This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.

Conclusions: We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

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
Journal of Neuro-Ophthalmology
Journal of Neuro-Ophthalmology 医学-临床神经学
CiteScore
2.80
自引率
13.80%
发文量
593
审稿时长
6-12 weeks
期刊介绍: The Journal of Neuro-Ophthalmology (JNO) is the official journal of the North American Neuro-Ophthalmology Society (NANOS). It is a quarterly, peer-reviewed journal that publishes original and commissioned articles related to neuro-ophthalmology.
期刊最新文献
Restricted Diffusion in Bilateral Superior Ophthalmic Vein Thrombosis. Antiphospholipid Antibody Syndrome: Concurrent Retinal Vein Occlusion and Homonymous Hemianopia From Ischemic Stroke. Ophthalmoplegia in Seropositive Myasthenia Gravis and Concomitant Seropositive Anti-GQ1b Disease. Optic Disc Drusen and Familial Exudative Vitreoretinopathy Phenotype: A Multimodal Imaging Perspective on a Rare Association. Reversible Junctional Scotoma From Pituitary Adenoma During Pregnancy.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1