{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:13:01Z","timestamp":1788415981408,"version":"build-2803163510"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is advancing rapidly across clinical medicine, yet its application within ophthalmic nursing, spanning emergency triage, perioperative care, day surgery, and patient education, remains poorly characterized. The growing burden of eye disease and the demand for community-based ophthalmic screening create an urgent context for understanding where AI can augment nursing capacity in this field and what barriers may be limiting evidence generation. This review aimed to systematically map published evidence on AI applications in ophthalmic nursing practice and education, characterize the technologies deployed and outcomes reported, and identify recurring research gaps and barriers through interpretive synthesis of the mapped literature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Four electronic databases (Web of Science Core Collections, PubMed, Scopus, and CINAHL) were searched from inception to June 12, 2026. Peer-reviewed English-language articles simultaneously addressing AI or machine learning, ophthalmology or eye conditions, and nurse engagement were eligible. Records were independently screened by two reviewers and data were extracted using a standardized charting form.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 254 records retrieved, 7 studies met the inclusion criteria (2023\u20132026). Applications spanned emergency triage and primary diagnosis, surgical nursing assistance (including one pre-clinical prototype without direct nurse involvement), day ward workflow management, and nursing education. Reported evaluation domains included triage or diagnostic performance, engineering detection performance, usability and acceptability, workflow efficiency, and educational outcomes. AI paradigms included classical machine learning, deep learning (convolutional neural networks), and large language models, deployed via clinical workflow interfaces, augmented reality, and robotic systems, and processing structured and multimodal data (image, text, audio).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Despite ophthalmology's prominent role in the early development of clinical AI, dedicated research on AI in ophthalmic nursing remains uncommon in the published literature. To our knowledge, this is the first scoping review to draw on international, English-language sources on this topic. Future research should prioritize nurse-led studies in community and primary care settings, broader modality exploration matched to nursing tasks, rigorous prospective evaluation of nursing-specific outcomes, and, where no current study exists, nurses as active co-designers of AI tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1943007","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:18:25Z","timestamp":1788412705000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Current landscape and research gaps in artificial intelligence for ophthalmic nursing: a scoping review"],"prefix":"10.3389","volume":"13","author":[{"given":"Qiong","family":"Song","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yamin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiqin","family":"She","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","year":"2019"},{"key":"B2","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41746-026-02418-7","article-title":"Advancing healthcare AI governance through a comprehensive maturity model based on systematic review","volume":"9","author":"Hussein","year":"2026","journal-title":"NPJ Digit Med"},{"key":"B3","year":"2020"},{"key":"B4","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1136\/emj.2007.057604","article-title":"Patterns of ophthalmological complaints presenting to a dedicated ophthalmic accident & emergency department: inappropriate use and patients\u2019 perspective","volume":"25","author":"Hau","year":"2008","journal-title":"Emerg Med J"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e489","DOI":"10.1016\/S2214-109X(20)30488-5","article-title":"The Lancet Global Health Commission on Global Eye Health: vision beyond 2020","volume":"9","author":"Burton","year":"2021","journal-title":"Lancet Glob Health"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"B7","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br J Ophthalmol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: towards a methodological framework","volume":"8","author":"Arksey","year":"2005","journal-title":"Int J Soc Res Methodol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.3980\/j.issn.1672-5123.2025.8.01","article-title":"Advances in artificial intelligence ophthalmic nursing in China over a decade","volume":"25","author":"Zhang","year":"2025","journal-title":"Int Eye Sci"},{"key":"B12","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s11701-024-02089-0","article-title":"Collaborative robot acting as scrub nurse for cataract surgery (CRASCS)","volume":"18","author":"Rekha","year":"2024","journal-title":"J Robot Surg"},{"key":"B14","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1080\/17538157.2025.2595015","article-title":"AI Chatbots vs. web-based learning in digital health: a randomized trial on improving dry eye syndrome knowledge for nursing students","volume":"51","author":"Dogan","year":"2026","journal-title":"Inform Health Soc Care"},{"key":"B15","doi-asserted-by":"publisher","first-page":"102331","DOI":"10.1016\/j.eclinm.2023.102331","article-title":"A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study","volume":"66","author":"Brandao-de-Resende","year":"2023","journal-title":"eClinicalMed"},{"key":"B16","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.ajo.2023.04.007","article-title":"EE-Explorer: a multimodal artificial intelligence system for eye emergency triage and primary diagnosis","volume":"252","author":"Chen","year":"2023","journal-title":"Am J Ophthalmol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"22490","DOI":"10.1038\/s41598-023-49213-y","article-title":"Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study","volume":"13","author":"Jindal","year":"2023","journal-title":"Sci Rep"},{"key":"B18","doi-asserted-by":"publisher","first-page":"20552076241269470","DOI":"10.1177\/20552076241269470","article-title":"AR-AI assisted ophthalmic nursing: preliminary usability study in clinical settings","volume":"10","author":"Huang","year":"2024","journal-title":"Digit Health"},{"key":"B19","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1080\/10447310802205776","article-title":"An empirical evaluation of the system usability scale","volume":"24","author":"Bangor","year":"2008","journal-title":"Int J Hum Comput Interact"},{"key":"B20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1097\/PTS.0000000000001295","article-title":"Intelligent verification tool for surgical information of ophthalmic patients: a study based on artificial intelligence technology","volume":"21","author":"Lin","year":"2025","journal-title":"J Patient Saf"},{"key":"B21","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.pcd.2026.04.007","article-title":"Comparative assessment of diagnostic agreement between artificial intelligence and general practitioners in diabetic retinopathy screening using non-mydriatic fundus photography","volume":"20","author":"Alcal\u00e1-Mill\u00e1n","year":"2026","journal-title":"Prim Care Diabetes"},{"key":"B22","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1186\/s12910-025-01265-7","article-title":"Evaluating trustworthiness in AI-based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges","volume":"26","author":"Chauhan","year":"2025","journal-title":"BMC Med Ethics"},{"key":"B23","doi-asserted-by":"publisher","first-page":"e47305","DOI":"10.2196\/47305","article-title":"Performance of the large language model ChatGPT on the national nurse examinations in Japan: evaluation study","volume":"6","author":"Taira","year":"2023","journal-title":"JMIR Nurs"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1608266","DOI":"10.3389\/fdgth.2025.1608266","article-title":"Research trends in the application of artificial intelligence in nursing of chronic disease: a bibliometric and network visualization study","volume":"7","author":"Du","year":"2025","journal-title":"Front Digit Health"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e70031","DOI":"10.1111\/ajr.70031","article-title":"Implementation of a new, mobile diabetic retinopathy screening model incorporating artificial intelligence in remote Western Australia","volume":"33","author":"Li","year":"2025","journal-title":"Aust J Rural Health"},{"key":"B26","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.3390\/jcm15041512","article-title":"Is artificial intelligence ready for emergency department triage? A retrospective evaluation of multiple large language models in 39,375 patients at a university emergency department","volume":"15","author":"Nedos","year":"2026","journal-title":"J Clin Med"},{"key":"B28","doi-asserted-by":"publisher","first-page":"e57566","DOI":"10.2196\/57566","article-title":"Effects of immersive technology-based education for undergraduate nursing students: systematic review and meta-analysis using the grading of recommendations, assessment, development, and evaluation (GRADE) approach","volume":"26","author":"Park","year":"2024","journal-title":"J Med Internet Res"},{"key":"B29","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abr\u00e0moff","year":"2018","journal-title":"NPJ Digit Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/1478-4491-12-44","article-title":"Mapping human resources for eye health in 21 countries of sub-Saharan Africa: current progress towards VISION 2020","volume":"12","author":"Palmer","year":"2014","journal-title":"Hum Resour Health"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1943007\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:18:27Z","timestamp":1788412707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1943007\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":30,"alternative-id":["10.3389\/fmed.2026.1943007"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1943007","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]},"article-number":"1943007"}}