{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T12:36:36Z","timestamp":1793018196423,"version":"4.5.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Photodiagnosis and Photodynamic Therapy"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.pdpdt.2026.105458","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:21:30Z","timestamp":1775100090000},"page":"105458","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["AI in ophthalmology: A bibliometric analysis of retinal imaging innovations and global research collaboration"],"prefix":"10.1016","volume":"59","author":[{"given":"Ruixi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seemab","family":"Gillani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.pdpdt.2026.105458_bib0001","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.3390\/diagnostics15121557","article-title":"Revolutionizing medical imaging: the transformative role of artificial intelligence in diagnostics and treatment","volume":"15","author":"Giansanti","year":"2025","journal-title":"Diagnostics"},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0002","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke vasc. Neurol."},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0003","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"issue":"9","key":"10.1016\/j.pdpdt.2026.105458_bib0004","doi-asserted-by":"crossref","first-page":"e888","DOI":"10.1016\/S2214-109X(17)30293-0","article-title":"Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis","volume":"5","author":"Bourne","year":"2017","journal-title":"Lancet Glob. Health"},{"issue":"8","key":"10.1016\/j.pdpdt.2026.105458_bib0005","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.7150\/thno.100786","article-title":"Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases","volume":"15","author":"Wang","year":"2025","journal-title":"Theranostics."},{"issue":"11","key":"10.1016\/j.pdpdt.2026.105458_bib0006","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","article-title":"Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis","volume":"128","author":"Teo","year":"2021","journal-title":"Ophthalmology."},{"issue":"2","key":"10.1016\/j.pdpdt.2026.105458_bib0007","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","article-title":"Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis","volume":"2","author":"Wong","year":"2014","journal-title":"Lancet Glob. Health"},{"key":"10.1016\/j.pdpdt.2026.105458_bib0008","series-title":"FDA Permits Marketing of Artificial Intelligence-Based Device to Detect Certain Diabetes-Related Eye Problems","year":"2018"},{"key":"10.1016\/j.pdpdt.2026.105458_bib0009","series-title":"WHO Issues First Global Report on Artificial Intelligence (AI) in Health and Six Guiding Principles for Its Design and use","year":"2021"},{"issue":"22","key":"10.1016\/j.pdpdt.2026.105458_bib0010","doi-asserted-by":"crossref","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"},{"issue":"2","key":"10.1016\/j.pdpdt.2026.105458_bib0011","doi-asserted-by":"crossref","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."},{"issue":"9","key":"10.1016\/j.pdpdt.2026.105458_bib0012","doi-asserted-by":"crossref","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"},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0013","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1038\/s41746-025-01959-7","article-title":"An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments","volume":"8","author":"Chen","year":"2025","journal-title":"NPJ Digit. Med."},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0014","doi-asserted-by":"crossref","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."},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0015","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.pdpdt.2026.105458_bib0016","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.jbusres.2021.04.070","article-title":"How to conduct a bibliometric analysis: an overview and guidelines","volume":"133","author":"Donthu","year":"2021","journal-title":"J. Bus. Res"},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0017","doi-asserted-by":"crossref","first-page":"852","DOI":"10.21037\/qims-24-1340","article-title":"Bibliometric analysis of research on the application of deep learning to ophthalmology","volume":"15","author":"Zhao","year":"2025","journal-title":"Quant. ImAging Med. Surg"},{"issue":"3","key":"10.1016\/j.pdpdt.2026.105458_bib0018","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1177\/1094428114562629","article-title":"Bibliometric methods in management and organization","volume":"18","author":"Zupic","year":"2015","journal-title":"Organ. Res. Methods"},{"issue":"2","key":"10.1016\/j.pdpdt.2026.105458_bib0019","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1096\/fj.07-9492LSF","article-title":"Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses","volume":"22","author":"Falagas","year":"2008","journal-title":"FASEB J."},{"issue":"152","key":"10.1016\/j.pdpdt.2026.105458_bib0020","article-title":"Comparing bibliometric analysis using PubMed, Scopus, and Web of Science databases","author":"AlRyalat","year":"2019","journal-title":"J. Vis. Exp. (JoVE)"},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0021","first-page":"959","article-title":"bibliometrix: an R-tool for comprehensive science mapping analysis","volume":"11","author":"Aria","year":"2017","journal-title":"J. Inf."},{"key":"10.1016\/j.pdpdt.2026.105458_bib0022","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.techfore.2018.07.006","article-title":"Knowledge management: a global examination based on bibliometric analysis","volume":"140","author":"Gaviria-Marin","year":"2019","journal-title":"Technol. Forecast. Soc. Change"},{"key":"10.1016\/j.pdpdt.2026.105458_bib0023","doi-asserted-by":"crossref","DOI":"10.1177\/01655515251373075","article-title":"Mapping internet technology and innovation performance: a bibliometric analysis","author":"Jiang","year":"2025","journal-title":"J. Inf. Sci."},{"issue":"2","key":"10.1016\/j.pdpdt.2026.105458_bib0024","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: vOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Van Eck","year":"2010","journal-title":"Scientometrics."},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0025","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1177\/016555158501000406","article-title":"Sources of information on specific subjects","volume":"10","author":"Bradford","year":"1985","journal-title":"J. Inf. Sci."},{"issue":"1","key":"10.1016\/j.pdpdt.2026.105458_bib0026","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/016555159001600105","article-title":"The duality of informetric systems with applications to the empirical laws","volume":"16","author":"Egghe","year":"1990","journal-title":"J. Inf. Sci."},{"issue":"19","key":"10.1016\/j.pdpdt.2026.105458_bib0027","first-page":"5","article-title":"Bradford law and related statistical patterns","author":"Garfield","year":"1980","journal-title":"Curr. contents"},{"issue":"12","key":"10.1016\/j.pdpdt.2026.105458_bib0028","first-page":"317","article-title":"The frequency distribution of scientific productivity","volume":"16","author":"Lotka","year":"1926","journal-title":"J. Wash. Acad. Sci."},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0029","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/0306-4573(85)90055-X","article-title":"Lotka's law: a testing procedure","volume":"21","author":"Pao","year":"1985","journal-title":"Inf. Process. Manage"},{"issue":"10","key":"10.1016\/j.pdpdt.2026.105458_bib0030","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEe Trans. Med. ImAging"},{"issue":"7","key":"10.1016\/j.pdpdt.2026.105458_bib0031","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology."},{"issue":"8","key":"10.1016\/j.pdpdt.2026.105458_bib0032","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.ophtha.2018.01.023","article-title":"Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs","volume":"125","author":"Li","year":"2018","journal-title":"Ophthalmology."},{"issue":"11","key":"10.1016\/j.pdpdt.2026.105458_bib0033","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","article-title":"Segmenting retinal blood vessels with deep neural networks","volume":"35","author":"Liskowski","year":"2016","journal-title":"IEEe Trans. Med. ImAging"},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0034","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s11023-018-9482-5","article-title":"AI4People\u2014An ethical framework for a good AI society: opportunities, risks, principles, and recommendations","volume":"28","author":"Floridi","year":"2018","journal-title":"Minds mach."},{"issue":"2","key":"10.1016\/j.pdpdt.2026.105458_bib0035","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.clsr.2017.12.002","article-title":"Clarity, surprises, and further questions in the article 29 Working Party draft guidance on automated decision-making and profiling","volume":"34","author":"Veale","year":"2018","journal-title":"Comput. Law Secur. Rev."},{"key":"10.1016\/j.pdpdt.2026.105458_bib0036","series-title":"International Conference on Medical image computing and computer-assisted intervention","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"issue":"22","key":"10.1016\/j.pdpdt.2026.105458_bib0037","doi-asserted-by":"crossref","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":"10.1016\/j.pdpdt.2026.105458_bib0038","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"5","key":"10.1016\/j.pdpdt.2026.105458_bib0039","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0040","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","article-title":"Fully automated detection and quantification of macular fluid in OCT using deep learning","volume":"125","author":"Schlegl","year":"2018","journal-title":"Ophthalmology."},{"issue":"8","key":"10.1016\/j.pdpdt.2026.105458_bib0041","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1109\/TMI.2019.2898414","article-title":"Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification","volume":"38","author":"Fang","year":"2019","journal-title":"IEEe Trans. Med. ImAging"},{"issue":"8","key":"10.1016\/j.pdpdt.2026.105458_bib0042","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1364\/BOE.8.003627","article-title":"ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks","volume":"8","author":"Roy","year":"2017","journal-title":"Biomed. Opt. Express"},{"issue":"3","key":"10.1016\/j.pdpdt.2026.105458_bib0043","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.ophtha.2019.09.036","article-title":"Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps","volume":"127","author":"Christopher","year":"2020","journal-title":"Ophthalmology."},{"key":"10.1016\/j.pdpdt.2026.105458_bib0044","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Schmidt-Erfurth","year":"2018","journal-title":"Prog. Retin. Eye Res"},{"issue":"4","key":"10.1016\/j.pdpdt.2026.105458_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.apjo.2024.100096","article-title":"Artificial intelligence for retinal diseases","volume":"13","author":"Lim","year":"2024","journal-title":"Asia-Pac. J. Ophthalmol."},{"issue":"9","key":"10.1016\/j.pdpdt.2026.105458_bib0046","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1055\/a-2378-6138","article-title":"Role of artificial intelligence in retinal diseases","volume":"241","author":"Mai","year":"2024","journal-title":"Klin. Monbl. Augenheilkd"}],"container-title":["Photodiagnosis and Photodynamic Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1572100026001250?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1572100026001250?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T12:18:26Z","timestamp":1793017106000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1572100026001250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":46,"alternative-id":["S1572100026001250"],"URL":"https:\/\/doi.org\/10.1016\/j.pdpdt.2026.105458","relation":{},"ISSN":["1572-1000"],"issn-type":[{"value":"1572-1000","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI in ophthalmology: A bibliometric analysis of retinal imaging innovations and global research collaboration","name":"articletitle","label":"Article Title"},{"value":"Photodiagnosis and Photodynamic Therapy","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.pdpdt.2026.105458","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"105458"}}