Keywords
Diabetic retinopathy
Diagnosis
Deep learning
Screening
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Abstract
Objective: To review the literature on the use of intelligent technologies, particularly artificial intelligence (AI), in the diagnosis of diabetic retinopathy. Methods: The study was conducted following the PVO strategy, focusing on patients with diabetic retinopathy and evaluating AI as a diagnostic tool. Searches were carried out in the PubMed Central (PMC) database using the terms "Artificial Intelligence" and "diabetic retinopathy." Articles in English and Portuguese, published between 2020 and 2024, were included. After applying the selection criteria, 14 articles were included in the final analysis. Results: AI demonstrated performance comparable to that of human specialists in detecting diabetic retinopathy, using techniques such as deep learning and convolutional neural networks. The main advantages include high accuracy, diagnostic efficiency, and potential to expand access to ophthalmic care. However, limitations such as cost, the need for training, and variability in image quality still pose challenges. Final considerations: AI has significant potential to improve the screening of diabetic retinopathy, but its implementation requires overcoming obstacles related to infrastructure and training. Future studies are necessary to adapt this technology to different clinical contexts.
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