THE ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATED DETECTION OF DIABETIC RETINOPATHY: CLINICAL AND PUBLIC HEALTH PERSPECTIVES
Abstract
Introduction and Objective: Diabetic retinopathy (DR) is a serious microvascular complication of diabetes and a leading cause of preventable blindness worldwide. With the global rise in diabetes prevalence, especially in low- and middle-income countries, early detection and timely treatment of DR have become critical. This paper reviews the evolving role of artificial intelligence (AI) in the automated detection of DR, evaluating its clinical effectiveness, implementation challenges, and potential impact on global preventive eye care.
Review Methods: A narrative review was conducted using PubMed, Scopus, and Web of Science databases to identify relevant literature published between 2016 and 2021. Search terms included combinations of “diabetic retinopathy,” “artificial intelligence,” “deep learning,” and “automated detection.” Articles were selected based on their relevance and contribution to advances, clinical applications, and challenges in AI-based DR screening.
State of Knowledge: Deep learning algorithms have demonstrated high accuracy in retinal image analysis, often matching expert ophthalmologists in detecting referable DR. AI enables scalable, rapid, and accessible screening, especially in regions with limited specialist availability. Challenges include data quality and diversity, algorithm transparency, patient privacy, clinical acceptance, and evolving regulatory frameworks.
Conclusion: AI represents a transformative opportunity to improve early diagnosis and management of diabetic retinopathy globally. To fully realize its benefits, ethical, technical, and regulatory issues must be addressed to ensure safe, effective, and equitable integration of AI into healthcare systems worldwide.
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