Shiv Garg, a member of the Student Leadership Johns Creek class of 2025 from Innovation Academy has developed EyeAI, an artificial intelligence (AI)-powered system poised to transform the global landscape of ocular disease diagnosis. EyeAI, which is designed to augment access to affordable and rapid eye health diagnostics, leverages state-of-the-art machine learning to detect ocular diseases based on retinal fundus images.
The World Health Organization reports that 2.2 billion people are affected by visual impairments worldwide, of which 1 billion cases are preventable; EyeAI presents an avant-garde solution to this growing global burden.
Shiv Garg is a high school student at FCS Innovation Academy, where he applies cybersecurity and machine learning to healthcare challenges. Shiv aims to continue developing technology-driven solutions to promote global health equity.
Traditional ophthalmologic care requires costly equipment and appointments with specialists; conversely, EyeAI offers a low-cost, web-based diagnostic tool that can be accessed from any location.
Thus, underprivileged communities can particularly benefit from this innovation as access to trained ophthalmologists is generally limited. As a method of preliminary diagnosis of disease, EyeAI users can directly access retinal imaging services or machines and upload retinal images to the EyeAI system to receive an almost-instant diagnostic result. This serves as a powerful tool for early disease detection, minimizing costs associated with diagnostic services.
“With the global population aging and the prevalence of diabetes on the rise, in addition to a disparity in ophthalmologist access, ocular diseases are becoming a major public health challenge,” said Shiv Garg, founder and developer of EyeAI. “EyeAI is designed to bridge the gap in healthcare access, providing communities with a low-cost, low-touch solution for early diagnosis of vision-threatening diseases.”
EyeAI’s potential to improve global health equity is vast, offering accessible healthcare to underserved populations and enabling the early identification of conditions that would otherwise lead to disease complications or blindness if undetected.