Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement.

IF 3 2区 医学 Q1 OPHTHALMOLOGY Current Opinion in Ophthalmology Pub Date : 2023-09-01 DOI:10.1097/ICU.0000000000000984
Daniel Shu Wei Ting, Mark S Humayun, Suber S Huang
{"title":"Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement.","authors":"Daniel Shu Wei Ting,&nbsp;Mark S Humayun,&nbsp;Suber S Huang","doi":"10.1097/ICU.0000000000000984","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership.</p><p><strong>Findings: </strong>Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data.</p><p><strong>Summary: </strong>A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.</p>","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/ICU.0000000000000984","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose of review: The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership.

Findings: Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data.

Summary: A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.

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
以人为中心的人工智能在眼科中的差距和未来:未来视力论坛共识声明。
回顾目的:未来愿景论坛讨论了以人为中心的计算的现状以及眼科数据收集、管理和整理的未来。尽管电子健康记录(EHR)系统和医疗数据数字化的采用令人鼓舞,但在实施全专业临床试验数据库方面仍存在障碍。本文确定了几个关键机遇,包括对图像元数据和数据标准化的需求,建立一个集中的试验数据库,激励临床医生和试验发起人参与,以及解决有关数据所有权的伦理问题。研究结果:克服这些挑战的建议包括使用医学数字成像和通信(DICOM)指南对图像元数据进行标准化,建立使用联邦学习(FL)的集中试验数据库,以及使用FL促进罕见病的跨机构合作。论坛教员表示,激励措施将加速人工智能、数字创新项目和数据共享协议的发展,从而使患者能够发布自己的数据。摘要:一个专业范围的临床试验数据库可以为疾病的自然史、病理生理学、试验失败的原因提供宝贵的见解,并改进未来的临床试验设计。然而,克服实施的障碍需要整个眼科社区利益相关者的持续讨论、合作和集体行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.80
自引率
5.40%
发文量
120
审稿时长
6-12 weeks
期刊介绍: Current Opinion in Ophthalmology is an indispensable resource featuring key up-to-date and important advances in the field from around the world. With renowned guest editors for each section, every bimonthly issue of Current Opinion in Ophthalmology delivers a fresh insight into topics such as glaucoma, refractive surgery and corneal and external disorders. With ten sections in total, the journal provides a convenient and thorough review of the field and will be of interest to researchers, clinicians and other healthcare professionals alike.
期刊最新文献
Ocular toxicities associated with antibody drug conjugates. Ocular manifestations of juvenile Sjögren's disease. Immune recovery uveitis: an ocular manifestation in HIV/AIDS receiving treatment. Ocular involvement in Steven-Johnson syndrome/toxic epidermal necrolysis: recent insights into pathophysiology, biomarkers, and therapeutic strategies. Artificial intelligence applications in ophthalmic surgery.
×
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