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

Clinical Snapshot

Key Findings

• Effect Size: Not applicable for viewpoint paper

• Primary Outcome: Framework classifying AI-enabled eye care pathways from direct human assessment to fully autonomous AI gatekeeping

• Nnt Or Sensitivity: Not applicable - conceptual framework development

Clinical Application

High feasibility for framework application, though implementation depends on health system structure and resources Highly relevant to Australian context given similarities to UK gatekeeper model with GP referrals, potential for integration with existing diabetic retinopathy screening programs, and consideration for PBS-funded AI diagnostic tools Healthcare systems implementing AI in ophthalmology, particularly relevant for retinal disease screening and cardiovascular risk assessment

Abstract

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.

References

1. 1.Poschkamp, B., Ong, A. Y., Engelmann, J., Grün, M., Stahl, A., Keane, P. A., & Merle, D. A. (2026). Rerouting eye care: How AI and telemedicine are reshaping ophthalmology patient journeys in the United Kingdom and Germany. Journal of Medical Internet Research. https://doi.org/10.2196/93170