Generative artificial intelligence for ophthalmic images: developments, applications and challenges

Generative Artificial Intelligence (GenAI) is revolutionizing ophthalmology imaging by addressing critical limitations in data availability, annotation costs, and clinical workflow automation. This review provides a comprehensive analysis of GenAI's technical innovations, clinical applications, and persistent challenges within the ophthalmic imaging domain. We first survey the evolution of generative architectures, from Generative Adversarial Networks to Diffusion Models and vision-language frameworks. These innovations enable novel applications including counterfactual pathology synthesis, longitudinal disease progression modeling, and post-treatment outcome visualization, which enhance diagnostic precision and patient engagement. We then systematically review methodological advancements in GenAI, with a focused analysis on key clinical application categories: image generation, cross-modal domain transfer, image enhancement, post-treatment prediction, image segmentation, and vision-language tasks. Finally, we critically evaluate generative models, evaluation methods, and persistent challenges, such as the need for standardized evaluation frameworks, anatomical fidelity validation, and equitable integration into global healthcare systems. By addressing these barriers, GenAI holds transformative potential to improve diagnostic accuracy, streamline personalized treatment workflows, and democratize access to high-quality ophthalmic care.