{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:31:57Z","timestamp":1787041917320,"version":"build-2736575974"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"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. Cell Dev. Biol."],"abstract":"<jats:p>Artificial intelligence is increasingly positioned as a means of addressing the growing burden of ophthalmic disease, particularly in settings where specialist resources are scarce and visual impairment remains preventable. Yet much of ophthalmic AI has remained confined to algorithmic validation, with limited translation into routine care because of persistent barriers in trust, interoperability, accountability, and clinical fit. This article proposes the AI-Augmented Ophthalmologist as a conceptual and practical framework for human-AI collaboration in eye care. Rather than treating AI as a replacement for clinical expertise or as a standalone diagnostic tool, the framework defines three complementary forms of augmentation: perceptual augmentation, which extends the clinician\u2019s capacity to interpret high-dimensional multimodal data; decisional augmentation, which supports risk prediction, treatment planning, surgical safety, and personalized management; and empathetic augmentation, which redistributes clinical capacity toward communication, shared decision-making, and patient engagement. By synthesizing evidence from clinical studies, implementation research, and emerging foundation-model applications, this review shows how AI can restructure fragmented ophthalmic workflows into a continuous, patient-centered loop spanning screening, diagnosis, treatment, and follow-up. The conceptual innovation of this framework lies in shifting ophthalmic AI from task-level automation to system-level collaboration, in which technical precision, clinical judgment, and humanistic care are integrated rather than separated. We further outline the conditions needed to move ophthalmic AI from algorithmic validation to real-world clinical integration: context-aware explainability, human-centered workflow design, adaptive regulation, continuous performance auditing, and equity-oriented deployment. The AI-Augmented Ophthalmologist offers a roadmap for moving ophthalmic AI beyond the digital showcase toward sustainable, trustworthy, and equitable clinical integration.<\/jats:p>","DOI":"10.3389\/fcell.2026.1902233","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:23:17Z","timestamp":1787041397000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The AI-Augmented Ophthalmologist for chronic ocular diseases: a patient-centered framework for human-AI collaboration"],"prefix":"10.3389","volume":"14","author":[{"given":"Si-Rong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Second Norman Bethune Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Second Norman Bethune Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Lin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Second Norman Bethune Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Xiangya Hospital and Hunan Key Laboratory of Ophthalmology, Central South University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang-Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Second Norman Bethune Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e14","DOI":"10.1016\/j.ophtha.2021.08.023","article-title":"Foundational considerations for artificial intelligence using ophthalmic images","volume":"129","author":"Abr\u00e0moff","year":"2022","journal-title":"Ophthalmology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1080\/09286586.2024.2434738","article-title":"Feasibility and patient experience of a pilot artificial intelligence-based diabetic retinopathy screening program in northern Ontario","volume":"32","author":"Bhambhwani","year":"2025","journal-title":"Ophthalmic Epidemiol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1186\/s13690-025-01757-3","article-title":"Assessing adoption of human and AI-enabled diabetic retinopathy screening in primary healthcare settings: findings from a pragmatic trial","volume":"83","author":"Chauhan","year":"2025","journal-title":"Archives Public Health"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e2517204","DOI":"10.1001\/jamanetworkopen.2025.17204","article-title":"AI workflow, external validation, and development in eye disease diagnosis","volume":"8","author":"Chen","year":"2025","journal-title":"Jama Netw. 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