ArticleNPJ digital medicine2025
A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the diagnosis and prognosis of ocular trauma: a systematic review.BMC ophthalmology · 2026Pooled it
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Effects of Type and Timing of Clinician-Facing AI Support on Patient Trust in Medical Consultations: 2 Vignette Experiments.Journal of medical Internet research · 2026Article
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Rapid Access Macular Screening and Evaluation: A Datasheet for a Dataset to Diagnose and Triage Macular Diseases.Ophthalmology science · 2026Article
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.Ophthalmology science · 2026Article
- Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany.Journal of medical Internet research · 2026Article
- Customizing AI-based screening with real-world data: Practical insights from diabetic retinopathy.Acta ophthalmologica · 2026Observational
- "Doing no harm" in the digital age: navigating tradeoffs and operational considerations for privacy-preserving deep learning in medicine.NPJ digital medicine · 2026Article
- Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.NPJ digital medicine · 2026Review
- Current challenges and the way forwards for regulatory databases of artificial intelligence as a medical device.NPJ digital medicine · 2026Review
- Article
- Me, my doctor and AI: a qualitative interview study on patients' requirements for trustworthy use of AI-CDSS in ophthalmology.BMJ digital health & AI · 2026Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- Dual site external validation of artificial intelligence-enabled treatment monitoring for neovascular age-related macular degeneration in England.Eye (London, England) · 2025Article
- Sociotechnical influences on the adoption and use of AI-enabled clinical decision support systems in ophthalmology: a theory-based interview study.BMC health services research · 2025Article
- Exploring the Dilemma of AI Use in Medical Research and Knowledge Synthesis: A Perspective on Deep Research Tools.Journal of medical Internet research · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified - 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection. 19% (7/36) did not have published evidence describing performance. For the remainder, 131 clinical evaluation studies (range 1-22/AIaMD) describing 192 datasets/cohorts were identified. Demographics were poorly reported (age recorded in 52%, sex 51%, ethnicity 21%). On a study-level, few included head-to-head comparisons against other AIaMDs (8%,10/131) or humans (22%, 29/131), and 37% (49/131) were conducted independently of the manufacturer. Only 11 studies (8%) were interventional. There is scope for expanding AIaMD applications to other ophthalmic imaging modalities, conditions, and use cases. Facilitating greater transparency from manufacturers, better dataset reporting, validation across diverse populations, and high-quality interventional studies with implementation-focused outcomes are key steps towards building user confidence and supporting clinical integration.
Identifiers
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Registered trials
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.