Artificial intelligence in retinal vein occlusion: Current applications, challenges, and future directions

Retinal vein occlusion (RVO) is a leading cause of visual impairment worldwide, underscoring the need for earlier detection, objective risk stratification and more personalized management. Recent advances in artificial intelligence (AI), particularly deep learning, have enabled automated analysis of retinal imaging modalities routinely used in RVO, including color fundus photography, optical coherence tomography (OCT), OCT angiography, and fluorescein angiography. We critically examine current AI applications in RVO, with a focus on disease detection, subtype classification, ischemia assessment, prognostic modeling and treatment-related decision support. Using a synthesis-oriented framework, we identify unifying methodological trends across studies and evaluate their clinical relevance and translational maturity. AI models demonstrate strong performance in experimental settings, particularly when leveraging lesion-centric representations and multimodal inputs, although most evidence remains limited to retrospective research environments. We further highlight key barriers to clinical adoption, including small and predominantly single-center datasets, limited external and prospective validation, inconsistent annotation and reporting standards. Ethical, regulatory and workflow integration challenges are also discussed. Finally, we outline future directions for RVO-specific AI development, emphasizing the need for multicenter validation, interpretability and clinically deployable tools that support, rather than replace, clinical decision-making.