Panorama tecnológico de la inteligencia artificial para el diagnóstico oftalmológico: una revisión de alcance y estudio de vigilancia tecnológica

Abstract

Introduction: Artificial Intelligence (AI) has become a key enabler in ophthalmic diagnostics, offering advanced capabilities for disease detection, medical image analysis, and clinical decision support. Its application is especially relevant in conditions such as diabetic retinopathy and glaucoma. However, the rapid growth of scientific literature, patents, and commercial tools makes it difficult to clearly understand the current technological landscape.
Objective: To map existing technological developments in AI-based ophthalmic diagnosis through a comprehensive Scoping Review complemented by a Technology Surveillance exercise, identifying trends, maturity levels, and innovation opportunities.
Method: The study followed the Arksey and O’Malley framework and the PRISMA-ScR guidelines. Evidence was collected and analyzed from scientific databases, patent records, commercial product catalogs, and software repositories. The review also assessed the maturity of identified solutions using Technology Readiness Levels (TRLs), a 1–9 scale developed by NASA to evaluate the development stage of a technology, from basic research to operational deployment.
Results: The findings identified deep learning and convolutional neural networks (CNNs) as predominant technological trends in AI-based ophthalmic diagnosis. The results also showed a transition from theoretical algorithms to commercially validated products with regulatory endorsement, such as IDx-DR and EyeArt. Nevertheless, important gaps remain in terms of accessibility, dataset diversity, regulatory integration, and device affordability, particularly in emerging economies.
Conclusions: AI-based ophthalmic diagnostic technologies are progressing toward validated and commercially available solutions. However, persistent economic and contextual barriers limit their adoption in regions such as Latin America. The study highlights strategic opportunities for innovation, especially in the development of low-cost, portable diagnostic tools adapted to underserved and resource-limited settings.