Evidence map›Paper›PMID 42202299›Full record

ArticleJournal of medical Internet research2026

Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany.

Broder Poschkamp, Ariel Yuhan Ong, Justin Engelmann, Michael Grün, Andreas Stahl, Pearse Andrew Keane, David Adrian Merle

Abstract readComparative Study
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Broder PoschkampDepartment of Ophthalmology, Universitätsmedizin Greifswald, Ferdinand Sauerbruch Street, Greifswald, Mecklenburg-Vorpommern, 17475, Germany, 49 3834865900.ORCID http://orcid.org/0000-0003-2393-0369
Ariel Yuhan OngInstitute of Ophthalmology, University College London, London, England, United Kingdom.ORCID http://orcid.org/0000-0001-9300-573X
Justin EngelmannInstitute of Ophthalmology, University College London, London, England, United Kingdom.ORCID http://orcid.org/0000-0002-5345-6023
Michael GrünInstitute of Ophthalmology, University College London, London, England, United Kingdom.ORCID http://orcid.org/0009-0000-4353-9557
Andreas StahlDepartment of Ophthalmology, Universitätsmedizin Greifswald, Ferdinand Sauerbruch Street, Greifswald, Mecklenburg-Vorpommern, 17475, Germany, 49 3834865900.ORCID http://orcid.org/0000-0002-8013-7597
Pearse Andrew KeaneInstitute of Ophthalmology, University College London, London, England, United Kingdom.ORCID http://orcid.org/0000-0002-9239-745X
David Adrian MerleInstitute of Ophthalmology, University College London, London, England, United Kingdom.ORCID http://orcid.org/0000-0003-4485-5580

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: 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.

Indexed as

Artificial IntelligenceEye DiseasesOphthalmologyTelemedicineGermanyHumansUnited KingdomAI screeningartificial intelligenceartificial intelligence as a medical deviceophthalmic AIophthalmic artificial intelligencepatient pathwaystele-ophthalmology

Identifiers

PMID42202299
PMCPMC13215633

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.