Artificial Intelligence-Based Medical Devices for Diabetic Retinopathy Screening in the European Union.

backgroundDiabetic retinopathy (DR) remains a leading cause of preventable blindness, yet screening programs across Europe face persistent workforce and capacity constraints amid rising diabetes prevalence. Artificial intelligence (AI)-enabled screening platforms have been developed to support scalable DR detection; however, their regulatory status, validation approaches, and implementation readiness vary considerably.

methodsWe conducted a targeted scoping review of 13 CE-certified AI systems for autonomous or semi-autonomous DR detection available in the European Union as of October 23, 2025 (IDx-DR, EyeArt, RetCAD, Mona DR, Retmarker DR, SELENA+, Remidio Medios AI, RetinoScan, Aireen DR, OphthAI, LuxIA, Airdoc-Eye DR, and Vistel). Data were charted across predefined domains, including device designation, regulatory classification, evidence sources, validation study design, reported diagnostic performance metrics, and implementation-related considerations. The review aimed to map the extent and nature of available evidence without conducting quantitative synthesis or comparative ranking.

resultsMost systems employed deep-learning-based fundus image analysis, often incorporating automated image-quality assessment. Reported sensitivities and specificities for referable DR (RDR) varied across systems, generally falling within ranges consistent with regulatory expectations; however, reporting standards and study designs were heterogeneous, limiting direct comparison. Several systems were supported by multicenter or prospective evaluations, while others relied primarily on retrospective datasets. A subset of platforms reported multi-disease detection capabilities. Evidence specific to sight-threatening DR (STDR) was less frequently described and demonstrated wider variability. Non-EU regulatory pathways were mentioned in some reports, but were outside the primary scope of this review. Other systems demonstrate high diagnostic accuracy in controlled evaluations, though performance for STDR remains limited (mean ≈ 80%), largely due to reliance on single-modality 2D fundus imaging without optical coherence tomography (OCT) integration. Implementation-related evidence, including workflow integration and monitoring requirements under the EU Medical Device Regulation (MDR), was limited across systems.

conclusionsCE-certified AI systems for DR detection represent a diverse and rapidly evolving landscape. While substantial progress has been made in regulatory classification and validation efforts, evidence remains heterogeneous, particularly for STDR detection and real-world implementation. Future research should prioritize consistent reporting standards, evaluation of multimodal approaches, and studies addressing real-world effectiveness to support safe and equitable deployment under the evolving EU regulatory framework.