From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening

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.