ReviewFrontiers in public health2026
From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) remains a preventable cause of vision loss as diabetes prevalence rises worldwide. Artificial intelligence (AI)-enabled retinal screening has progressed from retrospective validation to prospective and real-world deployment, with recent reviews showing high diagnostic performance across fundus photography, smartphone-based systems, and autonomous platforms. Yet diagnostic accuracy alone does not ensure public health benefit. Population impact can be lost when eligible people are not reached, images are ungradable, results are delayed, referrals are incomplete, treatment capacity is insufficient, or disadvantaged groups face financial, geographic, or digital barriers. Unlike reviews centered mainly on diagnostic accuracy or economic performance, this Mini Review reframes AI-enabled DR screening as a population-health intervention spanning detection through completion of effective eye care. We synthesize evidence on diagnostic validity, implementation, economics, referral completion, care cascades, patient-centered outcomes, equity, and governance. We argue that effectiveness and cost-effectiveness are properties of the complete screening-to-treatment pathway, not the algorithm alone, and propose a four-domain framework encompassing technical validity, service integration, population impact, and equity and sustainability. Future research should prioritize comparative implementation studies and outcome sets that capture screening reach, gradability, referral completion, treatment initiation, time to treatment, avoidable vision loss, vision-related quality of life, acceptability, costs, and subgroup equity. The policy question is no longer whether AI can detect DR, but whether health systems can translate automated detection into equitable completion of effective eye care at scale.
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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.