Authors: Khawlah Abdullah Almana , Ahmed Mahmoud Hassan , Abdulrahman Abdulaziz Alsughayyir , Wael Sulobi Alharbi , Hawraa Maki Alsadiq , Abdulrahman Mohammed Alamri , Yazeed Bader Alaql , Saad Khalid Aldawsari

Abstract

Background: Glaucoma is one of the leading causes of irreversible blindness worldwide and is often diagnosed at an advanced stage due to its asymptomatic early course. Recent advances in artificial intelligence (AI) have created new opportunities for improving the diagnosis, prediction, and management of glaucoma. This narrative review aims to provide an overview of current AI applications in glaucoma care, as well as recent developments and ongoing challenges.

Methods: A narrative review was conducted of studies published between 2019 and 2024, identified through a structured search of PubMed, Scopus, Web of Science, and Google Scholar. Studies focusing on AI applications in glaucoma diagnosis, disease progression prediction, and management were included.

Results: AI models, particularly deep learning and convolutional neural networks, have demonstrated high accuracy in detecting glaucomatous changes using fundus imaging, optical coherence tomography, and visual field data. These models also show promise in predicting disease progression and supporting clinical decision-making. Emerging technologies such as explainable AI, federated learning, and large language models aim to enhance interpretability, data security, and clinical applicability, although challenges related to data heterogeneity, limited external validation, and model transparency remain.

Conclusion: AI represents a promising approach for improving glaucoma care through enhanced diagnostic accuracy, risk stratification, and personalized management. Continued research, validation, and integration into clinical workflows are essential to ensure safe and effective real-world implementation.

Keywords: Artificial intelligence, glaucoma, deep learning, disease progression, ophthalmology.