Date of Graduation

Summer 8-20-2026

Document Access

Project/Capstone - Global access

Degree Name

Master of Public Health (MPH)

College/School

School of Nursing and Health Professions

Department/Program

Public Health

First Advisor

Kyle Knight

Abstract

A retinal image may reveal more than eye disease; it may also show early signs of chronic illness elsewhere in the body. Diabetes, hypertension, cardiovascular disease, and chronic kidney disease can develop before symptoms become noticeable, making earlier detection an important public health goal. Advances in artificial intelligence have created new possibilities for using retinal images to identify patterns linked to these conditions. This scoping review examined how Artificial Intelligence -assisted retinal imaging has been used for chronic disease detection, prediction, and screening, along with the practical and health-equity factors that may influence its use.

To map the current evidence, peer-reviewed studies were identified through PubMed, Scopus, CINAHL, Google Scholar, and reference-list searches. Study characteristics and findings were charted in Microsoft Excel and compared through narrative thematic synthesis.

Across the literature, diabetic retinopathy emerged as the most established use of Artificial- Intelligence assisted retinal imaging. These systems were most consistently used to identify patients who may need further eye care. Retinal images were also examined for cardiovascular risk, blood pressure, chronic kidney disease, type 2 diabetes, and other systemic conditions, but the evidence for these applications was less consistent. Performance varied across algorithms, imaging devices, patient populations, and image quality. Portable cameras and telemedicine may bring screening closer to patients in primary care, community, and lower-resource settings. However, their usefulness depends on trained staff, affordable services, clinician trust, reliable referral systems, and access to follow-up care. Few studies directly assessed whether these approaches reduced health disparities.

The central public health implication is that AI-assisted retinal imaging should be introduced as part of a complete screening and referral pathway that connects patients to timely diagnosis and appropriate care.

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