Researchers at the University of Florida, in collaboration with Meta, have demonstrated that artificial intelligence can analyze standard retinal photographs to predict key biological and lifestyle risk factors associated with Alzheimer’s disease. Published in the Journal of Alzheimer's Disease, the study leverages machine learning to process over 40,000 eye scans from a United Kingdom patient database, identifying subtle retinal variations that correlate with neurovascular health. Led by biomedical engineering professor Ruogu Fang, the research team, including Adam Woods and Meta researcher Yunchao Yang, showed that AI models can accurately predict demographic and physiological markers such as sex, blood pressure, smoking status, alcohol consumption, and insomnia. These indicators are traditionally captured through medical records or self-reporting, which often lack accuracy or completeness. Retinal imaging provides an objective, cumulative biological record of vascular and neurological changes, functioning as a low-cost screening tool. Because Alzheimer’s pathology develops over decades, early identification of these risk markers could enable preventive interventions, including lifestyle modifications and targeted therapies, before irreversible cognitive decline occurs. The study highlights the potential of repurposing routine ophthalmic exams for broader neurological screening, significantly expanding accessibility compared to expensive diagnostic modalities like MRI. This advancement positions retinal AI as a scalable, non-invasive biomarker for early Alzheimer’s risk stratification and public health monitoring.