Abstract Glaucoma is the leading cause of irreversible blindness worldwide, with early diagnosis and timely intervention being essential to preserve vision. Traditional diagnostic approaches, including optic disc assessment, perimetry, and optical coherence tomography (OCT), often face limitations such as interobserver variability and difficulty in detecting early disease. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool to address these challenges. This review summarizes current applications of AI in glaucoma, including automated analysis of fundus images, OCT-based detection of structural damage, visual field interpretation, risk stratification, and prediction of disease progression. Several AI models have demonstrated performance comparable to, and in some cases exceeding, expert clinicians. In addition, integration of multimodal datasets and tele-ophthalmology platforms offers promise for enhancing access to care in resource-limited settings. While promising, challenges remain, including limited external validation, algorithmic bias, regulatory hurdles, and concerns regarding ethical and medico-legal accountability. Future directions emphasize large-scale validation, integration into clinical workflows, and the potential for AI to complement rather than replace clinical judgment.
Mishra et al. (Wed,) studied this question.
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