This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.
Age range
22 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
DR screening completion and result
Timeframe: the enrollment (baseline) and through study completion, an average of 1 year
DR screening efficiency
Timeframe: the enrollment (baseline) and through study completion, an average of 1 year
Knowledge and attitudes about DR
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DM self-efficacy
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DM self-management
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DR Screening Satisfaction Survey (Intervention Group)
Timeframe: Baseline survey at study visit and follow up survey 12-months after (at next DR screening).
Demographic and Clinical Data
Timeframe: Intake at study 1 day visit.
Social Determinants of Health
Timeframe: Intake at study 1 day visit.