Purpose: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression.
Methods: Nonsystematic literature review using the search combinations "Artificial Intelligence," "Deep Learning," "Machine Learning," "Neural Networks," "Bayesian Networks," "Glaucoma Diagnosis," and "Glaucoma Progression." Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted.