The research project is looking to develop a foundational AI model capable of detecting a wide range of systemic diseases from retinal images, according to a statement released on Thursday by Australia’s Monash University, which is leading the study.

By using advanced AI to analyse retinal images linked with health data from hundreds of thousands of patients, the team aims to generate accurate, non-invasive screening tools for earlier diagnosis, treatment and prevention, it said.

It added that existing tools for detecting these conditions are often invasive, insufficiently personalised, or too costly to be widely used.

This research investigates an alternative approach: using the eye as a window to whole-body health. By applying oculomics – the science of detecting systemic disease biomarkers through the eye – the project seeks to enable earlier and more personalised detection of disease.

Instead of relying on an onerous manual analysis of large image datasets, this project uses advanced AI to build a multimodal model that detects multiple systemic diseases more comprehensively than single-disease approaches, said Monash University associate professor Ge Zongyuan.

“Research has shown that the retina provides a unique, non-invasive glimpse into the body’s vascular and neural system. We hypothesise that oculomics will help us develop rapid, non-invasive, cost-effective biomarkers to detect systemic diseases and predict future risks to prioritise treatment,” Ge added.

Optain Health president Zachary Tan, who is co-leading the study, said early identification through retinal imaging could enable timelier interventions and shift healthcare “towards prevention rather than treatment”.