AI Diabetic Retinopathy Screening

EyeCheckup supports AI-assisted retinal image analysis for diabetic retinopathy screening workflows. The page is designed for clinics, hospitals, diabetes programs, employers, insurers, and screening networks that need a scalable way to identify patients who may require follow-up eye care.

Why diabetic retinopathy screening is important

Diabetic retinopathy can progress before a patient notices vision changes. Regular retinal screening helps identify signs of disease earlier and supports timely referral. AI can help care teams scale this process by analyzing fundus images and supporting consistent triage.

How the workflow works

• Identify eligible patients with diabetes.
• Capture retinal fundus images using a compatible workflow.
• Check whether images are gradable.
• Run AI-assisted analysis.
• Generate a clear screening report.
• Refer positive or ungradable cases according to clinical protocol.

What healthcare teams should evaluate

Before adopting any AI screening tool, buyers should ask about intended use, supported cameras, ungradable image handling, sensitivity, specificity, validation population, privacy, reporting, and referral rules.

Implementation settings

AI diabetic retinopathy screening can support primary care clinics, diabetes centers, ophthalmology networks, hospitals, and mobile screening programs. The best results come when the technology is connected to a real operational pathway for follow-up.

Evidence-led communication

EyeCheckup should communicate carefully. AI screening can support detection and triage workflows, but it should not be described as a replacement for professional medical diagnosis outside its intended use. Strong content should explain both capabilities and limitations.

Request a demo

Contact EyeCheckup to discuss AI diabetic retinopathy screening workflows, deployment requirements, and partnership options.