Can a same-visit AI eye exam close a screening gap for young people with diabetes?
In a prospective US study of 397 young people, point-of-care autonomous AI brought exam completion to 99.5% among White participants and 98% among non-White participants. That closes a process gap—not yet a vision-outcome gap.
The short version
- What happened
- The prospective ACCESS2 study enrolled 397 young people: 72% had type 1 diabetes and 28% had type 2 diabetes.
- Who it affects
- Young people and their families may be able to complete a recommended retinal screen during the diabetes appointment instead of organising a separate visit. Health systems would still need reliable referral, follow-up and quality assurance after the AI result.
- What remains unproven
- This was a prospective, single-workflow study rather than a randomised comparison with another same-visit screening approach.
Keep in mind
This was a prospective, single-workflow study rather than a randomised comparison with another same-visit screening approach.
The sensitivity estimate has a wide 95% confidence interval of 67.6% to 100%, consistent with a small number of positive cases.
Completion does not establish that the programme prevents sight loss, improves long-term follow-up or works similarly in other health systems.
Digital Diagnostics supplied AI interpretations in kind; one author reports company roles, investment and relevant patents.
Source analysis · 1 linked source. Primary analysed source date: 23 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
Putting the exam inside diabetes care almost eliminated the completion gap
ACCESS2 recruited 397 White and non-White young people with diabetes during routine diabetes-care visits. Each participant received an autonomous-AI retinal exam and optical-coherence-tomography imaging at the point of care. Before enrolment, prior eye-exam completion differed sharply: 79.8% among White participants versus 61.8% among non-White participants. With the study workflow, 99.5% and 98% completed the AI exam, meeting the researchers' equivalence test.[1]
Completion was strong; diagnostic certainty was less complete
Compared with the study's standard grading for more-than-mild diabetic retinopathy, the AI system reported 100% sensitivity, but the 95% confidence interval ran from 67.6% to 100%, signalling few positive cases and substantial uncertainty. Specificity was 76.0% (95% CI 71.4% to 79.9%). A completed test is valuable only if its referrals and reassurances are dependable, so both accuracy and downstream follow-up matter.[1]
The distinction that matters
What ACCESS2 measured
The study links an access question to a diagnostic-accuracy check, but stops before patient outcomes.
Before the study
Check prior eye-exam completion
79.8% White; 61.8% non-White
Same-visit AI
Image and interpret during diabetes care
99.5% White; 98% non-White completed
Accuracy check
Compare AI with standard grading
100% sensitivity; 76% specificity, with uncertainty
This is evidence about access, not prevented sight loss
For families, a same-visit result can remove the need to arrange a separate specialist screening appointment. But the study did not compare AI with a same-visit human examiner, randomise participants to different workflows or measure later visual outcomes. It therefore cannot separate the effect of autonomous interpretation from the effect of bringing retinal imaging into the diabetes clinic. Digital Diagnostics supplied interpretations in kind, and a co-author is an investor, director and consultant at the company with relevant patents. Replication across health systems, transparent false-positive follow-up and longer-term outcomes would strengthen the case.[1]