Authors : Gursimran Singh

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/ycxee24k

Scribd : https://tinyurl.com/ms5ya9ny

DOI : https://doi.org/10.38124/ijisrt/26jan012

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence (AI) and machine learning (ML) have become groundbreaking technologies in the field of Ophthalmology and especially in the screening of diabetic retinopathy (DR) and glaucoma. This review discusses how AI/ML systems are currently applied, methodologies used, performance metrics, and clinical implementation issues of AI/ML systems in the detection of these vision-threatening conditions. Very recent deep learning algorithms have achieved the diagnostic accuracy of human experts, or more, with a sensitivity and specificity rate over 90% in many of the studies. However, there are challenges such as dataset bias, regulatory approval, clinical integration and cost-effectiveness that need to be investigated further. This review is a synthesis of evidence obtained from recent literature and discusses future directions of AI powered ophthalmic diagnostics.

Keywords : Artificial Intelligence, Machine Learning, Deep Learning, Diabetic Retinopathy, Glaucoma, Computer Aided Diagnosis, Retinal Imaging.

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