AI in ophthalmology: A bibliometric analysis of retinal imaging innovations and global research collaboration

The rapid advancement of artificial intelligence (AI) has profoundly transformed ophthalmic research, particularly in the diagnosis and management of retinal diseases. This study conducts a comprehensive bibliometric and science mapping analysis to elucidate the intellectual landscape, thematic evolution, and collaborative dynamics of AI applications in eye healthcare. Drawing on data spanning over two decades, we analyze publication trends, citation impact, keyword co-occurrence, and author/institutional networks to address three core research questions: (1) the trajectory of research growth and citation influence in AI-driven retinal diagnostics, (2) the collaborative influence of leading authors and institutions shaping the field, and (3) the conceptual structures and thematic clusters guiding intellectual development. Results reveal exponential growth in research output post-2015, with deep learning, optical coherence tomography (OCT), and diabetic retinopathy emerging as dominant themes. Co-authorship and co-citation networks highlight strong regional and institutional clusters, led by prolific entities such as the University of London and the University of California System. Thematic and factorial analyses uncover a gradual shift from foundational algorithmic studies to multimodal and therapy-focused innovations, with emerging themes including explainable AI, telemedicine, and personalized diagnostics. Despite robust growth, notable gaps persist in real-world clinical integration, regulatory frameworks, and representation from low-resource regions. This study not only maps the current intellectual terrain of AI in ophthalmology but also identifies critical avenues for future research to ensure equitable, interpretable, and clinically translatable AI solutions in eye care.