ArticleThe Australian journal of rural health2025
Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.
Article in The Australian journal of rural health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current landscape and research gaps in artificial intelligence for ophthalmic nursing: a scoping review.Frontiers in medicine · 2026Pooled it
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- Implementation of risk prediction and stratification approaches for ageing populations in Australian healthcare: a systematic review.The Lancet regional health. Western Pacific · 2026Review
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- Digital Transformation of Rural, Regional, and Remote Australian Hospitals: A Pragmatic Strategy for Introducing AI.The Australian journal of rural health · 2026Article
- Exploring plausible futures for artificial intelligence in rural healthcare: insights from participatory foresight methods.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveDiabetic retinopathy (DR) screening rates are poor in remote Western Australia where communities rely on outdated primary care-based retinal cameras. Deep learning systems (DLS) may improve access to screening, however, require validation in real-world settings. This study describes and evaluates the implementation of a new, mobile DR screening model that incorporates artificial intelligence (AI) into routine care.
designProspective, population-based study.
settingThe model was co-designed with local Aboriginal communities and implemented in the remote, Pilbara region of Western Australia. A research officer without formal healthcare qualification performed retinal screening aboard a Mercedes Sprinter Van using an automated retinal camera with integrated AI diagnostics. Patients received their diagnosis on-the-spot and completed an evaluation survey. A remote clinician provided supervision and on-the-spot telehealth consultation for referable disease.
participantsPeople with diabetes from the Pilbara region. MAIN OUTCOME MEASURE(S): Number of people screened, acceptability of AI to patients.
resultsFrom February to August 2024, DR screening was provided to 9 communities across the Pilbara region. 78 patients provided research consent, of which 56.4% were Aboriginal or Torres Strait Islanders. 10.3% of retinal photos had referable DR and 8.4% of photos were ungradable. 96% of patients were 'Happy with the use of AI'.
conclusionOur new model for AI-assisted DR screening was culturally safe, acceptable to patients and effective, demonstrating an 11-fold increase in screening rates compared to 2023 Pilbara data. In remote Australian settings, AI-assisted DR screening may overcome historical barriers to service provision and improve minimisation of preventable blindness.
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