This study provides a comprehensive review of how generative adversarial networks (GANs), a form of generative artificial intelligence, are being utilized in ophthalmology for image analysis and synthesis. Based on an analysis of 40 relevant studies sourced from major databases (PubMed, Ovid MEDLINE, and Google Scholar) up to October 30, 2022, the authors outline a wide array of GAN applications. These span various imaging modalities (OCT, fundus, angiography, MRI) and encompass tasks such as enhancing image quality, generating realistic synthetic data to augment limited datasets, translating between image types (like fundus to angiography), identifying diseases including glaucoma and meibomian gland dysfunction, automatically segmenting anatomical features (retinal vessels, corneal nerves), homogenizing MRI scans, and predicting patient appearance after surgery or therapy. The discussion emphasizes GANs considerable promise for boosting ophthalmic research, diagnostics, and clinical workflows but also carefully addresses persistent challenges including mode collapse, spatial inaccuracies, potential synthesis failures, the difficulty in interpreting GAN outputs, and ethical considerations, concluding that further rigorous validation is required for safe and effective clinical integration.