Describing the Performance and the Infrastructure Requirements of the Existing Artificial Intelligence (AI)-Based...

AI-based diabetic retinopathy (DR) screening algorithms have been evaluated in many countries and have shown promise in expanding access to screening, especially in low- and middle-income countries (LMICs). However, the literature lacks guidance on which algorithms are best suited for these settings. This umbrella review summarizes current evidence on the performance, infrastructure needs, and global implementation of AI-based DR screening tools. Following the Preferred Reporting Items for Systematic Review (PRISMA) guidelines, systematic reviews were identified through searches in PubMed, Embase, ScienceDirect, Scopus, and Google Scholar up to April 18, 2024. Eligible studies were reviewed, and findings were presented in tables and graphics. Twenty systematic reviews were included. Most algorithms were developed, validated, and used in high-income countries, with none developed or implemented in Africa. More than 400 algorithms were identified, of which 161 had some form of clinical validation, and 31 were validated in real-world settings. Sensitivity ranged from 66.0% to 100.0%, specificity from 59.5% to 98.7%, and AUROC from 87.8% to 99.1%. Only 12 algorithms have received regulatory approval, and 11 of them are currently used in clinical practice. AI-based DR screening models hold promise as diagnostic tools across diverse clinical settings, particularly where ophthalmic resources are limited. However, successful implementation depends on appropriate infrastructure, local validation, and regulatory support. Addressing the significant gaps in algorithm development and validation in Africa is essential to ensure equitable access and effective use of AI in DR screening.