Siddharth Nath, Ehsan Rahimy, Ashley Kras, Edward Korot
{"title":"The utility of artificial intelligence in ophthalmic clinical trials.","authors":"Siddharth Nath, Ehsan Rahimy, Ashley Kras, Edward Korot","doi":"10.1097/ICU.0000000000001172","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>The current article provides an overview of the utility of artificial intelligence approaches to aid in the design, recruitment, execution, and dissemination of ophthalmic clinical trials.</p><p><strong>Recent findings: </strong>Within the last decade, artificial intelligence has heralded a new age for ophthalmology, with novel applications habitually appearing within the literature. Though clinical trials are considered the gold standard for driving evidence-based practice, remarkably few studies have examined the potential for machine learning to augment the clinical trial pipeline. Clinical trials within ophthalmology often do not reach planned endpoints due to insufficient enrolment, cost overruns, and can lack reliability from unblinded outcome assessors. Ones that do, frequently take longer to enroll patients than intended. Artificial intelligence-based approaches have recently been shown to be effective in identifying eligible clinical trial participants using both imaging and text data.</p><p><strong>Summary: </strong>Given the key role of clinical trials in the advancement of ophthalmic clinical practice, trialists should consider the potential for artificial intelligence-powered tools to enhance the design, recruitment, and delivery of future studies.</p>","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-09-04","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.0000000000001172","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 current article provides an overview of the utility of artificial intelligence approaches to aid in the design, recruitment, execution, and dissemination of ophthalmic clinical trials.
Recent findings: Within the last decade, artificial intelligence has heralded a new age for ophthalmology, with novel applications habitually appearing within the literature. Though clinical trials are considered the gold standard for driving evidence-based practice, remarkably few studies have examined the potential for machine learning to augment the clinical trial pipeline. Clinical trials within ophthalmology often do not reach planned endpoints due to insufficient enrolment, cost overruns, and can lack reliability from unblinded outcome assessors. Ones that do, frequently take longer to enroll patients than intended. Artificial intelligence-based approaches have recently been shown to be effective in identifying eligible clinical trial participants using both imaging and text data.
Summary: Given the key role of clinical trials in the advancement of ophthalmic clinical practice, trialists should consider the potential for artificial intelligence-powered tools to enhance the design, recruitment, and delivery of future studies.
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.