Autonomous screening
Eyenuk
Autonomously analyzes retinal images to screen people with diabetes for diabetic retinopathy.
Designed for
Healthcare provider
Care setting
Primary care and eye screening
Specialty
Ophthalmology, Primary care
Our assessment
EyeArt has strong prospective pivotal evidence for autonomous diabetic retinopathy detection under defined conditions. The largest remaining questions concern real-world imageability, referral completion, equitable performance, and whether greater screening access translates into better vision outcomes.
Based on public sources. RIR did not independently test this product. This is not an endorsement or recommendation.
What the evidence supports
Reviewed sources establish an authorized autonomous screening workflow for defined diabetic retinopathy findings.
What remains uncertain
Performance in local populations, imageability rates, referral completion, and outcomes after screening require site-specific evaluation.
How it was studied
EyeArt version 2.1.0 was evaluated in a prospective multicenter cross-sectional study of 942 consenting adults at 15 primary and eye-care sites. Its outputs were compared with reading-center grading of a rigorous ETDRS-based reference standard.
Product facts
Sources reviewed
2 public sources represented in this evidence profile.
Evidence source base
Peer-reviewed pivotal evidence with company funding and involvement, plus regulatory records. Local workflow evidence remains separate.
Important limitation
Pivotal accuracy and imageability do not establish local referral completion, equity, camera and operator transportability, long-term vision outcomes, or current-version durability.
Questions to ask