The role of artificial intelligence in the diagnosis, monitoring, and management of retinal diseases: a review of the current literature
Keywords:
Artificial Intelligence, Age-Related Macular Degeneration, BiomarkersAbstract
Introduction: retinal diseases, such as diabetic retinopathy and age-related macular degeneration, represent major causes of irreversible visual disability, generating increasing pressure on health systems.
Objective: to analyze recent evidence on the applications of artificial intelligence in the diagnosis, monitoring, and management of retinal pathologies.
Methods: a systematic literature review was conducted in international databases, using key terms related to artificial intelligence, deep learning, and retina. Articles published in recent years addressing clinical applications, algorithm validation, and implementation challenges were selected. The analysis focused on identifying trends, methodological contributions, and reported limitations.
Development: deep learning algorithms have achieved accuracy comparable to or exceeding that of human experts in retinal image classification tasks. Advances are highlighted in automated detection of diabetic retinopathy, quantification of biomarkers in optical coherence tomography, and prediction of progression in macular degeneration. Applications have also been explored in glaucoma and retinopathy of prematurity, expanding the spectrum of clinical utility. However, challenges remain regarding model generalization, explainability of algorithmic decisions, integration into healthcare workflows, and ethical and legal implications.
Conclusions: artificial intelligence constitutes a promising tool to optimize the diagnosis and monitoring of retinal diseases. Its ethical and effective integration can improve equity, accessibility, and quality of care, consolidating a new paradigm in contemporary ophthalmology.
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References
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