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Methodological quality was assessed with the Mixed Methods Appraisal Tool, with most studies scoring 5/5 or 4/5. The review finds frequent evidence on availability, growing evidence on acceptability, and limited coverage of accessibility and service quality. Overall, AI may improve cost-effectiveness and patient access, and is well accepted by patients, but large-scale long-term trials are needed.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/impact-of-artificial-intelligence-on-the-availability-accessibility-acceptability-and-quality-of-ophthalmic-disease-screening-services-a-scoping-review/563675/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/impact-of-artificial-intelligence-on-the-availability-accessibility-acceptability-and-quality-of-ophthalmic-disease-screening-services-a-scoping-review/563675.png","ImageObject",442,249,{"name":88,"@type":89},"awa","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-24","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the main purpose of this scoping review?","Question",{"text":108,"@type":109},"To identify existing literature on AI use in eye disease screening and determine whether AI-assisted diagnostic technologies improve the availability, accessibility, acceptability, and quality of screening services.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How many studies were included and how was quality assessed?",{"text":113,"@type":109},"Forty-two original studies were included. Methodological quality was assessed using the Mixed Methods Appraisal Tool.",{"name":115,"@type":106,"acceptedAnswer":116},"What outcomes did the review find regarding AI-assisted screening?",{"text":117,"@type":109},"AI shows significant potential to improve cost-effectiveness and patient access, especially in remote or underserved regions, and is well accepted by patients with high satisfaction and improved referral compliance.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},563675,1792251221,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":73},3985747858093,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Review  \nImpact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review  \nSenlin Lin  , 1,2,3,4 Juzhao Zhang  , 1,2,3,5 Catherine Jan,6 Liping Li,7 Yajun Peng, 1,3 Dan Qian,8 Yao Yin,9 Mengjia Zhang, 10 Jianxiu Feng, 10 Mingguang He, 11  \nHaidong Zou 1,2,3,4,5  \n► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1136/bjo-2024-325174](10.1136/bjo-2024-325174)) .  \nFor numbered affiliations see end of article.  \nCorrespondence to  \nProfessor Haidong Zou; [zouhaidong@sjtu.edu.cn](zouhaidong@sjtu.edu.cn) and Professor Mingguang He; [mingguang.he@unimelb.edu.au](mingguang.he@unimelb.edu.au)  \nSL, JZ, CJ and LL contributed equally.  \nReceived 8 January 2024 Accepted 25 November 2025 Published Online First  \n13 December 2025  \n© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.  \nTo cite: Lin S, Zhang J, Jan C, et al. Br J Ophthalmol 2026;110:601–606 .  \nABSTRACT  \nThis scoping review examines the existing literature on the application of artificial intelligence (AI) in screening for eye diseases, with a focus on evaluating whether AIassisted diagnostic technologies enhance the availability, accessibility, acceptability and quality of screening services. 42 original studies were selected for in-depth analysis, including those employing health economic evaluations. Methodological quality was assessed using the Mixed Methods Appraisal Tool, with the majority of studies demonstrating high quality—24 scored 5/5, 15 scored 4/5 and the rest scored 3/5 . Among the included studies, 34 compared manual screening with either AIassisted or fully AI-driven approaches. Availability was the most frequently studied aspect (28 studies), followed by acceptability (12 studies), whereas accessibility and service quality were less commonly addressed. Overall, AI shows significant potential to improve the costeffectiveness of eye care services and enhance patient access, particularly in remote or underserved regions. It was also well-accepted by patients, with high satisfaction and improved referral compliance. The findings suggest that AI holds promise for advancing eye disease screening, although large-scale, long-term trials are needed to effectively integrate AI into the reconstruction of screening processes and the reshaping of eye health service systems.  \nINTRODUCTION  \nBlindness is one of the most feared medical conditions—second only to cancer and AIDS—and vision is considered the most important of all the senses.1 Ophthalmic diseases are a wide range of pathologies affecting the eye and associated tissues, including blinding eye diseases that can cause permanent vision loss. Recent trends in disease incidence have shown a shift from infectious to non-infectious aetiologies, with cataracts, glaucoma, diabetic retinopathy, macular degeneration and pathological myopia being the most common causes.2 3 These conditions are largely associated with metabolic and agerelated factors. Early detection of these eye diseases is crucial for improving patient outcomes,4–6 as they are associated with avoidable visual impairment. However, the quality of care for these eye diseases requires improvement, particularly in low- and middle-income countries where staffing, financial and time constraints can hinder the implementation  \nand sustainability of large-scale population-based screening programmes.7 8 Addressing these challenges is imperative to prevent avoidable visual impairment and improve the overall quality of ophthalmic care in these populations.  \nThe advent of digital technologies has presented new opportunities for the healthcare industry, offering potential solutions to some of its most pressing challenges.9 10 In eye care, where imaging examinations play","cbCaiohELHqxuPWa","https://ap.wps.com/l/cbCaiohELHqxuPWa","pdf",457148,6,"English","# Abstract\n# Introduction","[{\"question\":\"What is the main purpose of this scoping review?\",\"answer\":\"To identify existing literature on AI use in eye disease screening and determine whether AI-assisted diagnostic technologies improve the availability, accessibility, acceptability, and quality of screening services.\"},{\"question\":\"How many studies were included and how was quality assessed?\",\"answer\":\"Forty-two original studies were included. Methodological quality was assessed using the Mixed Methods Appraisal Tool.\"},{\"question\":\"What outcomes did the review find regarding AI-assisted screening?\",\"answer\":\"AI shows significant potential to improve cost-effectiveness and patient access, especially in remote or underserved regions, and is well accepted by patients with high satisfaction and improved referral compliance.\"}]","Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review | PDF",1791837338]