Zakieh Vahedian Ardakani , Mehran Zarei-Ghanavati , Hamid Riazi-Esfahani , Seyed Mehdi Tabatabaei , Mohammad Reza Mehrabi Bahar, Sadegh Ghafarian, Ahmad Masoomi,
Volume 83, Issue 1 (April 2025)
Artificial intelligence (AI) has emerged as a transformative force in modern medicine, with ophthalmology standing at the forefront of its clinical integration. Among ophthalmic disorders, glaucoma—a leading cause of irreversible blindness worldwide—presents unique opportunities and challenges for AI-based solutions due to its chronic, progressive nature and reliance on multimodal data, including structural and functional assessments. This review article offers a comprehensive synthesis of the current and emerging roles of AI in the detection, monitoring, and management of glaucoma. AI algorithms, particularly deep learning and machine learning models, have demonstrated exceptional capabilities in interpreting fundus photographs, optical coherence tomography (OCT) images, and visual field data to identify glaucomatous damage. These systems often approach or even exceed the diagnostic performance of human experts. Moreover, AI has shown significant promise in facilitating large-scale population-based screening, improving early detection rates, and addressing disparities in access to subspecialty care, particularly in low-resource and remote settings. In the monitoring of disease progression, AI tools are being developed to detect subtle structural or functional changes over time, predict future visual outcomes, and support more precise and individualized treatment decisions. Despite these advancements, the widespread clinical adoption of AI in glaucoma care faces several critical barriers. Key limitations include poor generalizability of models across diverse populations, imaging devices, and clinical settings; scarcity of well-annotated, high-quality, and demographically representative datasets; and a lack of transparency and interpretability in algorithmic decision-making—commonly referred to as the “black box” problem. Ethical concerns, regulatory uncertainty, integration challenges within existing healthcare infrastructures, and medico-legal accountability also require thoughtful resolution before AI can be reliably deployed in clinical practice. This review critically evaluates the strengths, limitations, and real-world potential of AI technologies in glaucoma. It provides clinicians, researchers, and healthcare policymakers with a balanced and up-to-date perspective, highlighting promising avenues for future research, including explainable AI, federated learning, multi-modal data integration, and longitudinal validation studies. By fostering a deeper understanding of both the opportunities and challenges associated with AI, this article aims to guide the responsible, equitable, and evidence-based integration of AI into comprehensive glaucoma care.
