Artificial Intelligence in Glaucoma: A Clinician’s Guide to Diagnosis, Monitoring, and the Future

Artificial intelligence (AI) is rapidly becoming a valuable tool in glaucoma care, a field where early diagnosis and reliable detection of progression remain significant challenges. Glaucoma is a leading cause of irreversible blindness worldwide, with a particularly high burden in countries like India due to delayed diagnosis and limited access to specialized care. AI systems, especially those based on machine learning and deep learning, can analyze large volumes of imaging and functional data quickly and consistently. In glaucoma, AI has shown strong potential in automated detection of glaucomatous optic neuropathy from fundus photographs and optical coherence tomography, prediction of disease progression, screening in peripheral settings, and estimation of surgical outcomes. More recently, multimodal AI models that integrate imaging, visual fields, and clinical data have improved risk stratification and personalized care planning. However, challenges related to data quality, interpretability, generalizability, and medicolegal responsibility remain. AI should therefore be viewed as a decision support tool that complements, rather than replaces, clinical judgment. This review explains key AI concepts in simple terms and discusses emerging clinical applications relevant to glaucoma practice.