Inteligência artificial no rastreamento da retinopatia diabética: acurácia diagnóstica, detecção precoce e desafios para implementação na atenção primária à saúde

Diabetic retinopathy is a preventable cause of vision loss whose screening is hindered by the shortage of specialists, particularly in primary health care (PHC). This article analyzed, through a narrative review, 20 studies published between 2021 and 2026 on the use of artificial intelligence (AI) in screening for the disease, organized into three axes: diagnostic accuracy, early detection, and implementation challenges. Meta-analyses in real-world settings reported pooled sensitivity between 87% and 94% and specificity between 89% and 90%, but a head-to-head comparison of seven commercial algorithms revealed significant differences in performance. Prospective studies in population programs and Brazilian investigations with handheld cameras maintained sensitivity above 90%, with specificity ranging from 54% to 95.4%. Randomized trials showed that immediate feedback raised referral adherence from 39.6% to 51.5% in adults and exam completion from 22% to 100% in youth, in addition to increasing specialist productivity by about 40%. Among the challenges for PHC are the representativeness of training data, device dependence, the impact of false positives on specialized services, and the need for economic evaluations using local parameters. AI is technically feasible for expanding screening, but its incorporation into the Brazilian Unified Health System depends on local validation and effective referral pathways.