The eye as a digital biomarker: Transforming systemic disease assessment

TY - JOUR

T1 - The eye as a digital biomarker

T2 - Transforming systemic disease assessment

AU - Xu, Yanwu

AU - Trucco, Manuel

N1 - © 2026 The Authors.

PY - 2026/3/17

Y1 - 2026/3/17

N2 - 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 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 full potential of the eye as a window to systemic health will require aligning algorithmic innovation with clinical relevance, interpretability, and trust.

AB - 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 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 full potential of the eye as a window to systemic health will require aligning algorithmic innovation with clinical relevance, interpretability, and trust.

KW - Oculomics

KW - Systematic diseases

KW - Digital biomarkers

KW - Artificial intelligence

KW - Retinal imaging

KW - Foundation models

U2 - 10.1016/j.imed.2026.03.002

DO - 10.1016/j.imed.2026.03.002

M3 - Editorial

SN - 2096-9376

JO - Intelligent Medicine

JF - Intelligent Medicine

ER -