The eye is no longer confined to the study of isolated ophthalmic diseases; instead, it is emerging as a scalable, noninvasive source of biomarkers for systemic health assessment. Advances in ophthalmic imaging and artificial intelligence (AI) have revealed that retinal structure, microvasculature, and neurovascular coupling encode clinically meaningful information about cardiovascular, metabolic, and neurodegenerative disorders. This growing body of evidence challenges the traditional organ-centric view of ophthalmology and positions retinal biomarkers as a foundation for population-level risk stratification and longitudinal disease monitoring. In this Editorial, we argue that the true clinical impact of retinal AI will not be realized through incremental improvements in prediction accuracy, but from a paradigm shift toward clinically validated, human-trustworthy, and interpretable multimodal foundation models (FMs) for ophthalmology. Despite promising results across multiple systemic diseases, translation remains hindered by substantial modality heterogeneity, insufficient clinical validation, and limited interpretability. These barriers have constrained retinal AI largely to retrospective studies, preventing its adoption as a reliable clinical decision-support tool. We highlight key priorities for the field, including multimodal and longitudinal modeling, biologically grounded feature learning, human-AI collaboration, and rigorous prospective validation. Addressing these challenges is essential for transforming retinal imaging from a diagnostic adjunct into a cornerstone of precision medicine. Ultimately, realizing the eye’s full potential as a window to systemic health will require aligning algorithmic innovation with clinical relevance, interpretability, and trust.
Xu et al. (Sun,) studied this question.