Abstract / Summary
ABSTRACT Artificial intelligence (AI) has advanced rapidly in glaucoma, but many models remain optimized for case‐control detection rather than the clinically harder task of distinguishing glaucoma from its mimickers. This narrative review primarily examined literature published from January 2018 to March 2026 on glaucoma AI, difficult comparator groups, external validation, uncertainty, and reporting quality. High myopia, physiologic cupping, congenital disc anomalies, compressive optic neuropathy, and non‐arteritic anterior ischemic optic neuropathy remain major sources of diagnostic ambiguity. Recent studies show that machine learning can differentiate some of these entities, particularly on optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA), yet such comparator‐rich designs remain uncommon. Landmark detection models still lose performance under dataset shift, and recent reviews document persistent deficiencies in transparency and early clinical reporting. The most clinically useful direction for glaucoma AI is therefore not detection alone, but mimicker‐aware validation, clinically grounded reference standards, uncertainty‐aware outputs, and multimodal decision support.