{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:41Z","timestamp":1785545021129,"version":"3.56.0"},"reference-count":39,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) as a medical device is now progressively entering routine ophthalmic care, yet its impact is still mostly evaluated in terms of diagnostic performance rather than how it reshapes patient care pathways. This viewpoint argues that careful pathway design is crucial to implementing AI in ophthalmology so that it translates into real, practical benefits for patients and services. We propose a framework that classifies AI- and telemedicine-enabled eye care pathways by the initial point of contact and the role of AI: from direct human assessment to grader- or telemedicine-based use to AI-first contact and fully autonomous AI gatekeeping. We apply this framework to 2 contrasting health systems, the United Kingdom and Germany, focusing on common retinal diseases and cardiovascular risk assessment from retinal images. In the United Kingdom, AI is being introduced mainly as a modular upgrade to standardized programs layered onto a gatekeeper model centered on community optometrists and general practitioners. In Germany, direct access to office-based ophthalmologists, opportunistic screening, and commercial retail offerings are producing more fragmented clinician- and market-driven AI adoption. Our comparison shows that the same AI technologies can generate very different patient journeys across health systems. Therefore, their value depends not only on diagnostic performance but also on intentional pathway design, including clear escalation rules and structured information transfer. This includes appropriate task shifting from ophthalmologists to trained staff or new roles, such as image grading, tele-ophthalmology triage, or pathway management. Without such design, AI risks duplicate testing, incomplete or poorly coordinated referrals, and further fragmentation of care.<\/jats:p>","DOI":"10.2196\/93170","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:11:24Z","timestamp":1779916284000},"page":"e93170-e93170","source":"Crossref","is-referenced-by-count":0,"title":["Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2393-0369","authenticated-orcid":false,"given":"Broder","family":"Poschkamp","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-573X","authenticated-orcid":false,"given":"Ariel Yuhan","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5345-6023","authenticated-orcid":false,"given":"Justin","family":"Engelmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4353-9557","authenticated-orcid":false,"given":"Michael","family":"Gr\u00fcn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8013-7597","authenticated-orcid":false,"given":"Andreas","family":"Stahl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9239-745X","authenticated-orcid":false,"given":"Pearse Andrew","family":"Keane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4485-5580","authenticated-orcid":false,"given":"David Adrian","family":"Merle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","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","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01726-8","article-title":"A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America","volume":"8","author":"Ong","journal-title":"NPJ Digit Med"},{"issue":"11","key":"R3","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.4103\/ijo.IJO_1442_21","article-title":"Telemedicine in diabetic retinopathy screening in India","volume":"69","author":"Ramasamy","journal-title":"Indian J Ophthalmol"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1136\/bmjophth-2021-000735","article-title":"Predictors of ophthalmology career success (POCS) study","volume":"6","author":"Das","journal-title":"BMJ Open Ophthalmol"},{"issue":"9","key":"R5","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s00347-019-0894-2","article-title":"Ophthalmology 2019-where do we stand?: An analysis of the treatment situation in Germany [Article in German]","volume":"116","author":"Schuster","journal-title":"Ophthalmologe"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101971","article-title":"Applications of deep learning in fundus images: a review","volume":"69","author":"Li","journal-title":"Med Image Anal"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-018-0324-8","article-title":"A renaissance of teleophthalmology through artificial intelligence","volume":"33","author":"Korot","journal-title":"Eye"},{"key":"R8","unstructured":"Wilson JM Jungner G . 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