Purpose of review Ophthalmology has emerged as the proving ground for medical artificial intelligence (AI) because of its imaging-centric workflows, standardized data acquisition, and well defined clinical endpoints. Recently, the field has started transitioning from narrowly trained, task-specific algorithms toward multimodal models that are trained on large datasets and can be adapted to many tasks. These models may integrate information from images, text, and clinical data and are increasingly trialed in real-world settings. This review examines recent advances and ongoing controversies in the use of AI as a basic science, translational, and clinical research tool in ophthalmology. Recent findings Recent literature highlights the growing role of self-supervised and foundation models for scalable phenotyping, biomarker discovery, and cross-task generalization, particularly in retinal imaging and optical coherence tomography (OCT). In parallel, large-scale clinical deployments, including cloud-based OCT platforms and autonomous diabetic retinopathy screening, demonstrate clinical impact while revealing persistent challenges related to workflow integration and patient generalizability. Regulatory and ethics-focused studies underscore increasing attention to validation rigor, bias, and postdeployment monitoring, exposing heterogeneity in research standards and physician practices. Summary Ophthalmic AI is maturing toward real-world clinical implementation. Future progress will depend less on performance gains alone and more on clinical alignment, rigorous validation, equity-aware evaluation, and postdeployment surveillance, alongside continued investment in basic science to advance disease understanding and inform robust, generalizable model development.
Miller et al. (Tue,) studied this question.