Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program
Open accessAbstract
Purpose: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows. Patients and Methods: A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow (“Human Workflow”), an AI-based workflow involving IDx-DR (“AI Workflow”), and a two-step hybrid workflow (“AI–Human Hybrid Workflow”). The AI Workflow provided same-day results, whereas the other workflows took 1– 5 days. Patients were surveyed by phone about follow-up decisions. Results: Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% ( N =48) of patients who followed up chose their location based on primary care referral. Under the Human Workflow and AI–Human Hybrid Workflow, 12.0% ( N =14/117) and 11.7% ( N =12/103) of patients with a referrable screening result followed up compared to 35.5% of patients under the AI Workflow ( N =99/279; χ 2 df=2 = 36.70, p < 0.00000001). Conclusion: Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI–Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result. Keywords: telemedicine, teleophthalmology, fundus photography, deep learning, machine learning, referral
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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 [15]
The Methods (Program Overview; page unavailable) say the program did not alter established referral-placement processes: patients were referred to ophthalmology when indicated under both AI and human workflows. This contradicts the characterization of this study as replacing routine referrals with targeted, AI-assisted referrals. The Results report greater follow-up under the AI workflow, but do not make the citing authors’ cross-study effect-size comparison. (LLM, conf=0.85)
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 [15]
The Methods (Program Overview; page not specified) describe a real-world diabetic retinopathy screening program in the San Francisco Bay Area that adopted an AI workflow after its first 18 months, which began in September 2019. The paper supports an example of implementation after 2020, but does not establish that implementation accelerated globally since 2020. (LLM, conf=0.85)
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 [15]
The Methods (Program Overview; page unavailable) describe the human workflow operating for the first 18 months, followed by the AI workflow. The Discussion acknowledges potential bias in the retrospective analysis and confounding from comparison with a historical cohort. These passages support the concern about temporal confounding attributed to Dow et al., but the paper does not characterize its risk of bias as serious; it also describes a hybrid comparison intended to reduce confounding. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDow et al.'s retrospective study (n=396) showed 35.5% referral uptake with AI workflow versus 12.0% with manual grading when restricting analysis to university eye institute appointments (+23.5%, p=0.000004).
“Dow et al.‘s retrospective study (n = 396) showed 35.5% referral uptake with AI workflow versus 12.0% with manual grading when restricting analysis to university eye institute appointments (+23.5%, p = 0.000004)15.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [15]
The Results report 35.5% (99/279) follow-up at the university eye institute under the AI Workflow versus 12.0% (14/117) under the Human Workflow; those denominators total 396, and the difference is 23.5 percentage points. The study describes its analyses as retrospective (Discussion). However, the Results give the pairwise comparison as p < 0.000004, not p = 0.000004. (LLM, conf=0.88)
Experimental — accuracy not yet measuredWorkflow redesign alone may drive gains in referral uptake in some contexts, even with existing manual grading infrastructure such as asynchronous telehealth via remote grading centres.
“suggesting workflow redesign alone may drive gains in referral uptake in some contexts, even with existing manual grading infrastructure such as asynchronous telehealth via remote grading centres.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [15]
The Discussion (page not provided) suggests that faster release of results and changes to patient messaging may improve follow-up. But the observed three-fold gain occurred with an AI workflow compared with manual-grading workflows; the study did not test workflow redesign alone while retaining manual grading. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Dow et al. achieved a 23.5% improvement in referral uptake without documented co-interventions for behaviour change.
“Dow et al. achieved substantial improvement ( + 23.5%) without documented co-interventions for behaviour change15”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [15]
The Results report follow-up at the university eye institute of 35.5% under the AI workflow versus 12.0% under the human workflow—a 23.5-percentage-point difference, not a 23.5% relative improvement in referral uptake generally. The Methods (Program Overview) say the prescribed workflow differences concerned how and when results were communicated, supporting the absence of a documented separate behaviour-change intervention. (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 [15]
The Results report that follow-up rose from 12.0% under the Human Workflow to 35.5% under the AI Workflow, a three-fold increase. This supports the example of substantial improvement from a low starting rate, but the paper does not compare health systems or establish that baseline performance determines the size of gains. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn settings with pre-existing manual grading workflows, AI-assisted DR screening provided incremental optimisation rather than transformation.
“Conversely, in settings with pre-existing manual grading workflows, AI-assisted DR screening provided incremental optimisation rather than 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 [15]
The Methods (Program Overview; page unavailable) describe an existing human-graded workflow and a faster AI workflow, supporting the comparison. But the Results (page unavailable) report approximately three times higher ophthalmology follow-up under AI; the paper does not characterize this as merely incremental optimisation or assess whether it was transformative. (LLM, conf=0.83)
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 [15]
The Results (page not available) count ungradable images among referable screening results in the Human and AI–Human Hybrid workflows: their referral denominators are 117 and 103, respectively. The AI Workflow denominator of 279 includes only MTMDR-positive results, so this paper does not show ungradable images retained in every referral denominator it reports. The paper also cannot establish the cited sentence’s count of three studies. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDow and colleagues reported improved follow-up in an AI-based workflow, but the result was linked to workflow differences, result timing, and patient communication rather than the algorithm alone.
“Dow and colleagues reported improved follow-up in an AI-based workflow, but the result was linked to workflow differences, result timing, and patient communication rather than the algorithm alone (20).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [20]
The 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 [15]
Studies comparing AI-assisted screening to existing manual grading workflows showed modest gains ranging from +11.8 to +23.5%.
“Conversely, studies comparing AI-assisted screening to existing manual grading workflows showed modest gains ranging from +11.8 to +23.5%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 [15]
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 [15]
In settings with pre-existing manual grading workflows, AI-assisted DR screening yielded modest improvements (+11.8-23.5%) despite higher baseline performance (39-77%), except Dow et al. (baseline 12%).
“yielding modest improvements ( + 11.8–23.5%)14–16,18 despite higher baseline performance (39–77%)14,16,18 save for Dow et al. (baseline of 12%)15.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [15]
Findings 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 [15]
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- Authors
- Dow, E. R., Chen, K. M., Zhao, C., Knapp, A., Phadke, A., Weng, K., Do, D. V., Mahajan, V. B., Mruthyunjaya, P., Leng, T., & Myung, D.
- Affiliations
- Duke University (US)
- Stanford University (US)
- Stanford Health Care (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Myung, David
- ORCID
- Venue
- Clinical Ophthalmology
- Publisher
- Informa UK Limited
- Published
- 2023-11
- Volume
- Volume 17
- Pages
- 3323-3330
- ISSN
- 1177-5467 (ISSN-L)
- 1177-5483
- PMC
- PMC10665027
- OpenAlex
- W4388153243
- Cited by
- 29
- References
- 18
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 6
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Clinical ophthalmology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Research to Prevent Blindness (EY026877, P30-EY026877)
- Regeneron Pharmaceuticals
- Stanford Diabetes Research Center
- Apellis Pharmaceuticals
- Astellas Pharma
- National Institutes of Health (EY 026877)
- Genentech
- National Eye Institute (P30EY026877)
- 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, artificial intelligence, teleophthalmology, telemedicine, follow-up rates, ophthalmology referral, fundus photography, deep learning, type 1 and type 2 diabetes, primary care referral
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