Artificial intelligence oculomics for systemic health and longevity medicine: 2025 and beyond.

Abstract

Purpose of review: With the rise of 'oculomics' and the application of advanced artificial intelligence techniques in healthy ageing, retinal imaging, the only way we can directly visualize the microvascular circulation, is expanding beyond ophthalmology into broader systemic health monitoring. The purpose of this review is to summarize recent advances in this rapidly evolving field and assess the opportunities, challenges, and future directions of the use of oculomics in translating into real-world clinical use.

Recent findings: Retinal imaging modalities, such as color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and wide-field imaging, are increasingly integrated with deep learning algorithms to detect, predict, and manage a broad spectrum of systemic diseases, including cardiovascular, cerebrovascular, renal, metabolic, and neurodegenerative disorders, as well as less commonly studied conditions. While research in more established areas is beginning to address clinical translation and implementation, significant challenges remain before these technologies can be reliably adopted in long-term, real-world healthcare settings.

Summary: Artificial intelligence applied to retinal imaging has matured from proof-of-concept classifiers to externally validated, occasionally regulated tools that noninvasively profile systemic conditions. Multiplexed foundation models and multimodal transformers herald a shift toward holistic 'oculomics' platforms, yet prospective multicenter trials, equitable performance auditing, and health-economic evaluations are essential before widescale clinical adoption.