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The discussion contrasts combined performance with standalone AI and early-year residents, highlighting gains in glaucoma-suspect classification accuracy and retinal sensitivity, as well as tracking of CDR estimates. It also addresses educational implications, particularly how reduced cognitive load and altered disease exposure may affect trainees’ clinical judgment.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/commentary-synergistic-ai-resident-approach-achieves-superior-diagnostic-accuracy-in-tertiary-ophthalmic-care-for-glaucoma-and-retinal-disease/417514/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/commentary-synergistic-ai-resident-approach-achieves-superior-diagnostic-accuracy-in-tertiary-ophthalmic-care-for-glaucoma-and-retinal-disease/417514.png","ImageObject",300,407,{"name":92,"@type":93},"Logic","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main finding discussed in the commentary?","Question",{"text":112,"@type":113},"The commentary emphasizes that combining AI systems with resident physicians can achieve superior diagnostic accuracy for glaucoma and retinal disease in a real tertiary-care setting.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the commentary compare AI performance to first-year residents?",{"text":117,"@type":113},"It notes that AI alone outperformed first-year residents on measures including glaucoma-suspect classification accuracy and retinal disease sensitivity, while also explaining limitations related to image quality for CDR evaluation.",{"name":119,"@type":110,"acceptedAnswer":120},"What concerns does the commentary raise about using AI in training settings?",{"text":121,"@type":113},"It argues that when trainees rely on AI outputs, educational risk may shift toward reduced cognitive effort and narrower disease exposure, potentially changing how clinical judgment develops.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},417514,1790765824,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24,"preview_page_count":4},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","TYPE General Commentary PUBLISHED 19 December 2025 DOI 10.3389/fopht.2025.1749411  \nOPEN ACCESS  \nEDITED BY  \nMichael Wormstone,  \nThe University of Nottingham Ningbo (China), China  \nREVIEWED BY  \nAlessio Martucci,  \nUniversity of Rome Tor Vergata, Italy  \n*CORRESPONDENCE  \nRosa Corro  \n [rosaacorro@gmail.com](rosaacorro@gmail.com)  \nRECEIVED 19 November 2025  \nREVISED 02 December 2025  \nACCEPTED 08 December 2025  \nPUBLISHED 19 December 2025  \nCITATION  \nWilliams-Gaona K and Corro R (2025) Commentary: Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease.  \nFront. Ophthalmol. 5:1749411 .  \ndoi: 10.3389/fopht.2025.1749411  \nCOPYRIGHT  \n© 2025 Williams-Gaona and Corro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nCommentary: Synergistic  \nAI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease  \nKevin Williams-Gaona 1 and Rosa Corro 2*  \n1Caja de Seguro Social (CSS) Dr. Hugo Spadafora, Colón, Panama, 2 Department of Research, Mayo Clinic Florida, Jacksonville, FL, United States  \nKEYWORDS  \nartificial intelligence, clinical training, diagnostic workflow, medical education, ophthalmology  \nA Commentary on:  \nSynergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease  \nBy Camacho-Garca-Forment D, Bayln-Vzquez G, Arriozola-Rodrguez K, Avalos-Ramirez E, Hartleben-Matkin C, Valdez-Flores H, Hodelin-Fuentes D and Noriega A (2025) Front. Ophthalmol. 5:1581212 . doi: 10 .3389/fopht.2025.1581212  \nCamacho-Garćıa-Forment́ı et al. present an impressive demonstration of how a synergistic collaboration between artificial intelligence systems and resident physicians can improve diagnostic accuracy in glaucoma and retinal disease. Not only do they show that an AI-resident partnership can outperform either one alone, they also demonstrate something that many groups struggle to capture: how these systems behave in a real tertiary-care environment. Anyone who has worked in a busy ophthalmology service knows that elegant results on paper are one thing, and making them coexist with high patient load, mixed pathology, and time pressure is another. Their team managed both.  \nEven though the combined approach was the top performer, the study also shows that AI on its own outperformed first-year residents across several key measures, including higher accuracy in glaucoma suspect classification (88.6% vs. 82.9%) and much higher sensitivity for retinal disease (76% vs. 52%, and 100% for high-risk findings) . AI’s CDR estimates also tracked more closely with expert measurements than those of residents (r = 0.728 vs. 0.538), although the system could only evaluate CDR in 61.6% of patients because of image-quality issues. That detail matters: even when an algorithm performs well, realworld imaging is variable, and someone still needs to handle the cases it cannot process.  \nAs we read through their results, a central question emerged. We talk a lot about“human in the loop” AI, and it’s usually framed as a reassuring idea. The algorithm assists, but the clinician remains in control. That logic works today, when clinicians have years of pattern recognition behind them. But what happens when the human entering the loop has had fewer chances to build the very skills the loop depends on?  \nFrontiers in Ophthalmology 01 [frontiersin.org](frontiersin.org)  \nOphthalmology is built on repetition. Residents grow by seeing normal variants, borderline OCTs, unusu","cbCaijdqEOsPr4ea","https://ap.wps.com/l/cbCaijdqEOsPr4ea","pdf",295650,"English","# Key results and comparison\n## AI alone vs residents vs synergistic AI-resident partnership\n# Educational implications for trainees\n## Cognitive effort and altered exposure to disease spectrum\n# Opportunities for deliberate system design\n## Teaching modes and explainable AI","[{\"question\":\"What is the main finding discussed in the commentary?\",\"answer\":\"The commentary emphasizes that combining AI systems with resident physicians can achieve superior diagnostic accuracy for glaucoma and retinal disease in a real tertiary-care setting.\"},{\"question\":\"How does the commentary compare AI performance to first-year residents?\",\"answer\":\"It notes that AI alone outperformed first-year residents on measures including glaucoma-suspect classification accuracy and retinal disease sensitivity, while also explaining limitations related to image quality for CDR evaluation.\"},{\"question\":\"What concerns does the commentary raise about using AI in training settings?\",\"answer\":\"It argues that when trainees rely on AI outputs, educational risk may shift toward reduced cognitive effort and narrower disease exposure, potentially changing how clinical judgment develops.\"}]","Commentary - Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease | PDF",1790609452]