EC Ophthalmology

Integrating ocular biomarkers with artificial intelligence (AI) technologies offers a transformative approach to systemic risk assessment in clinical practice. This article explores the potential of ocular biomarkers as predictive tools for systemic diseases, leveraging AI's robust analytical capabilities. Ocular images, being non-invasive and rich in physiological information, can reveal early signs of systemic conditions such as cardiovascular disease, diabetes, and neurodegenerative disorders. AI-powered algorithms enhance these biomarkers' precision and predictive power, enabling early detection and monitoring of disease states that might otherwise remain undiagnosed until advanced stages. We review state-of-the-art AI methods in analyzing retinal images and other ocular data, highlight significant breakthroughs in automatization, and assess the challenges and ethical considerations of integrating AI in clinical risk assessment. The convergence of these technologies promises to refine individual patient care and advance large-scale public health strategies by facilitating more accurate and timely systemic disease prediction. This paper delineates the current landscape and prospects of ocular biomarker utilization in AI-driven systemic health assessments.

 Keywords: Ocular Biomarkers; Artificial Intelligence; Systemic Risk Assessment; Retinal Imaging; Disease Prediction

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