A Review of the Use of Artificial Intelligence in Ophthalmology Imaging: Approximation to Ocular Histopathology

Ophthalmic imaging has advanced to a level at which it can closely approximate key histopathological features of certain ocular tissues, changing the way ocular disease is screened for, diagnosed, and monitored. Modalities such as optical coherence tomography (OCT), anterior segment OCT (AS-OCT), OCT angiography (OCTA), and in vivo confocal microscopy (IVCM) capture tissue architecture with high-resolution structural and even microvascular information. In this review, the authors summarize the current evidence on artificial intelligence (AI) based applications that utilize high-resolution ophthalmic imaging and digital pathology to support near-histologic interpretation across a range of ocular diseases. Reported diagnostic performance and concordance with histopathologic findings are examined while describing the technical, biological, and regulatory boundaries of AI-derived inference. While AI supports objective, scalable, and reproducible analyses of ophthalmic imaging data, limitations in resolution, generalizability, and specificity prevent its use as an independent substitute for tissue-based diagnosis where histopathologic confirmation is required.