Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care
Open accessAbstract
Purpose Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes. Design Prospective cohort study. Participants Adults 18 years of age or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients. Methods All participants underwent nonmydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared with the historical adherence rate from the same clinic. Main outcome measure Rate of adherence to eye screening recommendations. Results By automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% showed inconclusive results. The remaining 48.9% showed negative screening results, confirmed by human overread, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (confidence interval, 92.3%-100%) in detecting an abnormal screening results, whereas its specificity was 65.7% (confidence interval, 57.0%-73.7%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1 year compared with the historical adherence rate of 18.7% (P < 0.0001, Fisher exact test). Conclusions Implementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.
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There was marked variation in timeframes for referral among included studies, ranging from 2 weeks for Li et al. in China to 12 months for Liu et al. in the USA.
“There was marked variation for timeframes among included studies ranging from 2 weeks for Li et al. in China18 up to 12 months for Liu et al. in the USA17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods (page not provided) specify referral appointments within 1 month for vision-threatening disease and within 3 months for referable disease or inconclusive results—not 12 months. The 12-month period in the Methods and Results is a window for measuring attendance after referral, not a recommended referral timeframe. This verdict concerns only the timeframe attributed to Liu et al.; it does not assess the comparison with Li et al. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredStudies comparing AI to routine referral approaches with lower baseline rates (18.7-22.0%) showed the largest effect sizes (+36.7% to +42.0%), while studies comparing AI to existing manual screening workflows with higher baseline rates (39.6-77.3%) showed more modest improvements (+11.8% to +19.4%).
“Studies comparing AI to routine referral approaches with typically lower baseline rates of uptake (18.7–22.0%) showed the largest effect sizes (+36.7% to +42.0%)13,17, while studies comparing AI to existing manual screening workflows with higher baseline rates of uptake (39.6–77.3%) showed more modest improvements (+11.8% to +19.4%)14,16,18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Split into 2 atoms — 0 confirmed, 0 partially supported, 1 disputed, 0 misattributed, 0 unverifiable, 1 not checked. Expand the claim to see per-atom verdicts.
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredReferral appointment timeframes varied from two weeks to twelve months and definitions of referable DR differed.
“Referral appointment timeframes varied from two weeks18 to twelve months17 and definitions of referable DR differed.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods (page unavailable) instruct patients with vision-threatening disease to attend within one month and those with referable disease within three months—not twelve months. The paper’s twelve-month period is an endpoint for measuring attendance, not a recommended referral appointment timeframe. It defines referable disease but does not itself establish that definitions differed across studies. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredReal-world implementation of diabetic retinopathy screening with artificial intelligence-assisted care pathways has accelerated globally since 2020.
“Real-world implementation of DR screening with AI-assisted care pathways has accelerated globally since 20208,13–22, yet evidence on clinical and health system outcomes such as referral uptake remains limited.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods describe AI-assisted screening in one St. Louis primary care clinic, with patients recruited from January to August 2018. This supports an example of real-world implementation, but not the claim that implementation accelerated globally since 2020. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe included studies had two distinct status quo care pathways: routine referrals for people with diabetes without prior screening and manual grading workflows including asynchronous teleophthalmology.
“It should be noted there were two distinct, status quo care pathways: studies providing routine referrals for people with diabetes without prior screening13,17 and manual grading workflows including asynchronous teleophthalmology14–16,18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods and Results describe a historical primary-care cohort of adults with diabetes, including patients due for an eye-screening examination, whose adherence to eye-care recommendations was compared with that of patients receiving automated screening. The Discussion describes referral for an eye examination without prescreening as a comparator, but the paper does not establish that routine referrals were provided to all patients in the historical cohort. The separate manual-grading pathway is attached to other citations in the citing sentence, not this source. (LLM, conf=0.80)
Experimental — accuracy not yet measured
Liu et al.'s prospective cohort study (n=1066) comparing AI screening to historical routine referral controls demonstrated 55.4% uptake at 12 months versus 18.7% in controls (+36.7%, p<0.0001), though this included proactive scheduling calls as a co-intervention.
“Liu et al.‘s prospective cohort study (n = 1066) comparing AI screening to historical routine referral controls demonstrated 55.4% uptake at 12 months versus 18.7% in controls (+36.7%, p < 0.0001), though this included proactive scheduling calls as a co-intervention17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThis system-level transformation yielded large effect sizes (+36.7-42.0% absolute improvement in referral uptake).
“This system-level transformation yielded large effect sizes ( + 36.7–42.0% absolute improvement in referral uptake)13,17”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Abstract Results report 55.4% adherence to follow-up eye care at one year among referred patients, versus 18.7% in historical controls—an absolute difference of 36.7 percentage points. The paper does not report the 42.0-point end of the cited range. Its Discussion also cautions that selection bias limits a causal interpretation of the comparison. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBaseline health system performance influenced the marginal gains, with settings with higher baseline rates of uptake experiencing relatively lower effect size, in contrast to lower-performing systems with greater margins for improvement.
“Thirdly, baseline health system performance appeared to influence the marginal gains, following a law of diminishing returns whereby settings with higher baseline rates of uptake experienced relatively lower effect size14,16,18, in contrast to lower-performing systems with greater margins for improvement13,15,17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Results report low historical adherence at the clinic (18.7%) and higher follow-up after automated screening; the Discussion describes the clinic as having a historically low screening rate. The paper studies one clinic and does not establish that higher-baseline health systems experience smaller gains or that baseline performance determines effect size across settings. Page numbers were not provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFacilitated scheduling addresses social opportunity barriers.
“Facilitated scheduling addresses social opportunity barriers16,17”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods (page unavailable) describe staff calling patients to schedule follow-up ophthalmology appointments, with repeat calls after unsuccessful contact. The Discussion reports improved attendance for the overall screening program, but the paper does not identify social opportunity barriers or establish that facilitated scheduling itself addressed them. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFindings from the review suggest the status quo for DR screening involves asynchronous teleophthalmology with delayed feedback of results or no prior screening as was the case in two North American studies by Wolf et al. and Liu et al.
“However, findings from our review suggest the status quo for DR screening involves asynchronous teleophthalmology with delayed feedback of results14–16,18 or no prior screening as was the case in two North American studies by Wolf et al. and Liu et al.13,17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods describe a historical comparison cohort whose adherence to eye-care recommendations was assessed without point-of-care automated screening, supporting the claim if “no prior screening” means no prescreening at the comparison visit. But the Results say only 644 of 974 historical patients were due for a screening examination; the paper does not establish that the cohort had never previously been screened. The study therefore supports a narrower description than “no prior screening.” (LLM, conf=0.83)
Experimental — accuracy not yet measuredTwo studies compared AI-assisted targeted referrals to routine referral pathways for all people with diabetes, in contrast to the remaining four studies whose comparators included manual grading via asynchronous teleophthalmology.
“First, two studies compared AI-assisted targeted referrals to routine referral pathways for all people with diabetes13,17, in contrast to the remaining four studies whose comparators included manual grading via asynchronous teleophthalmology14–16,18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods and Results (pages not provided) compare AI-screened patients referred according to their results with a historical cohort of adults with diabetes from the same clinic. The historical cohort was assessed for adherence to eye-care recommendations, but the paper does not establish that everyone in it received a routine referral; it also notes that only 644 of 974 were due for a screening exam. The clause about the other four studies is not attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThree studies included ungradable images as referable DR, thus retaining them in the referral denominator.
“Three studies included ungradable images as referable DR, thus retaining them in the referral denominator15–17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
The Methods say patients with inconclusive screening results were referred, and the Results say positive and inconclusive results were pooled when assessing abnormal screening results. But the Results list inconclusive results separately from referable diabetic eye disease; the paper does not classify those images as referable DR. The claim that three studies used this approach cannot be established from this source alone. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLiu et al., Dow et al., and Chotcomwongse et al. demonstrated serious risk of bias due to confounding from co-interventions, sequential implementation with potential for temporal confounding, and fundamentally different comparators.
“Liu et al.17, Dow et al.15, and Chotcomwongse et al.16 demonstrated serious risk of bias due to confounding from co-interventions (automated reminders16, proactive scheduling calls17), sequential implementation with potential for temporal confounding15,17, and fundamentally different comparators (AI-targeted versus routine referrals)17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Liu et al. conducted a scheduling phone call within two weeks of the AI screening appointment to arrange a referral appointment.
“Similarly, Liu et al. conducted a scheduling phone call within two weeks of the AI screening appointment to arrange a referral appointment17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
All studies consistently demonstrate improved referral uptake after AI-assisted DR screening.
“While all studies consistently demonstrate improved referral uptake after AI-assisted DR screening13–18, magnitude of benefit varies substantially from moderate improvements ( + 11.8%)16 to large effects ( + 42.0%)13.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Rates of referral uptake across these studies ranged from 55.4% to 89.1% and effect sizes ranged from +11.8% to +42.0%.
“Rates of referral uptake across these studies ranged from 55.4%17 to 89.1%16 and effect sizes ranged from +11.8%16 to +42.0%13.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
In Liu et al., there was a correlation between time and rates of uptake, increasing from 32.6% at 3 months to 55.4% at 12 months.
“In this study there was a correlation between time and rates of uptake, increasing from 32.6% at 3 months to 55.4% at 12 months17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Liu et al. reported a +36.7% improvement in referral uptake in a primary care clinic screening adults with diabetes.
“whilst Liu et al. reported a + 36.7% improvement in a primary care clinic screening adults with diabetes17.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Several studies included co-interventions alongside AI-assisted diabetic retinopathy screening with variable effectiveness.
“Several studies included co-interventions alongside AI-assisted DR screening with variable effectiveness13,16–18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Two studies that replaced routine referrals with targeted, AI-assisted referrals showed greater effect sizes than studies with a manual grading comparator.
“Firstly, comparator types whereby two studies replaced routine referrals with targeted, AI-assisted referrals13,17 showed greater effect sizes than those studies with a manual grading comparator14–16,18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Co-intervention usage varied across studies, with some utilising automated reminders, scheduling calls, or providing patient education alongside AI screening.
“Secondly, co-intervention usage varied across studies, with some utilising automated reminders16, scheduling calls17, or providing patient education alongside AI screening13,18.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Larger effect sizes in Wolf et al. (+42.0%) and Liu et al. (+36.7%) may reflect simultaneous targeting of multiple behaviour change domains, combining immediate feedback with scheduling support and patient education, or DR care pathway transformation.
“However, larger effect sizes in Wolf et al. ( + 42.0%)13 and Liu et al. ( + 36.7%)17 may reflect simultaneous targeting of multiple behaviour change domains, combining immediate feedback with scheduling support and patient education, or as a consequence of DR care pathway transformation.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
In settings where pre-existing workflows included routine referral strategies, AI-assisted DR screening enabled transformation of care pathways by replacing routine referrals with targeted, evidence-based referrals.
“In settings where pre-existing workflows included routine referral strategies13,17, AI-assisted DR screening enabled transformation of care pathways by replacing routine referrals with targeted, evidence-based referrals.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
Co-interventions (facilitated follow-up scheduling, text reminders, patient education, grading reports) were implemented alongside AI in most studies, precluding attribution of effects to specific pathway components.
“Third, co-interventions (facilitated follow-up scheduling, text reminders, patient education, grading reports) were implemented alongside AI in most studies13,16–18, precluding attribution of effects to specific pathway components.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [17]
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Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care
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Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program
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InfluentialSelf-citationTransparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review
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CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review
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“Wolf et al. reported a + 42.0% improvement in a diabetology clinic screening youth with diabetes 13 , whilst Liu et al. reported a + 36.7% improvement in a primary care clinic screening adults with diabetes 17 .”
InfluentialCode-Free Machine Learning for the Detection of Common Ophthalmic Diseases
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Advances in Teleophthalmology Screening for Diabetic Retinopathy
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AI Diabetic Retinopathy Screening in a Primary Care Setting in Rural Maine
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- Authors
- Liu, J., Gibson, E., Ramchal, S., Shankar, V., Piggott, K., Sychev, Y., Li, A. S., Rao, P. K., Margolis, T. P., Fondahn, E., Bhaskaranand, M., Solanki, K., & Rajagopal, R.
- Affiliations
- Washington University in St. Louis (US)
- University of Maryland, Baltimore (US)
- Eyenuk (United States) (US)
- Corresponding author
- Rajagopal, Rithwick
- ORCID
- Venue
- Ophthalmology Retina
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 5
- Issue
- 1
- Pages
- 71-77
- ISSN
- 2468-6530 (ISSN-L)
- PMC
- PMC8546907
- OpenAlex
- W3036347230
- Cited by
- 88
- References
- 34
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 13 · 2023: 19 · 2022: 9 · 2021: 2 · 2020: 1
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Ophthalmology Retina (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Research to Prevent Blindness
- National Eye Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy screening, automated retinal analysis, artificial intelligence, non-mydriatic fundus photography, point-of-care screening, primary care, referral adherence, low-income patients, screening sensitivity and specificity
- MeSH terms
- Ambulatory Care Facilities, Artificial Intelligence, Diabetic Retinopathy, Female, Follow-Up Studies, Humans, Image Processing, Computer-Assisted, Male, Mass Screening, Middle Aged, Primary Health Care, Prospective Studies, Retina
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