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Cited by 11Retinal Imaging and AnalysisRetinal Diseases and TreatmentsOphthalmology and Visual Health Research

Artificial Intelligence improves follow-up appointment uptake for diabetic retinal assessment: a systematic review and meta-analysis

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Rahmati, Masoud, Smith, Lee, Piyasena, Mapa Prabhath, Bowen, Michael, Boyer, Laurent, Fond, Guillaume, Kazemi, Abdolreza, Yon, Dong Keon +4 more·2025·Eye
DOI 10.1038/s41433-025-03849-4PMC PMC12325731
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Abstract

Background/Objectives: Artificial intelligence (AI) assessment of diabetic retinopathy (DR) instead of scarce trained specialists could potentially increases the efficiency and accessibility of screening programs. This systematic review aims to systematically examine the uptake of follow-up appointments with initial computer-based AI and human graders of DR. Methods: We conducted a systematic review and meta-analysis by screening articles in any languages in PubMed, MEDLINE (Ovid), EMBASE, Web of Science, Cochrane CENTRAL and CDSR published from database inception up to 20 th August 2024. We used random-effects meta-analysis to pool the results as odds ratios (OR) with corresponding 95% confidence intervals (CI). Results: Data from a total of 20,108 patients with diabetes (6476 participants graded using AI and 13,632 participants graded by human-graders; age range of the participants 5 to 67 years) from six studies were included. The result of the pooled meta-analysis showed that initial AI assessment of DR significantly increased uptake of follow-up appointments compared to human grader-based (OR = 1.89, 95% CI 1.78–2.01, P = 0.00001). Conclusions: The present systematic review and meta-analysis suggest that initial AI-based algorithm for screening DR is associated with an increased uptake of follow-up examination. This is most likely due to instant results being made available with AI based algorithms when compared to a delay in assessment with human graders.

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  • Rahmati et al.'s meta-analysis did not explore mechanisms underpinning differences in referral uptake.

    “A recent meta-analysis of four studies13–15,17 by Rahmati et al.23 showed positive effects of AI-assisted DR screening on referral uptake, but did not explore mechanisms underpinning these differences.”

    From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [23]

    The Discussion explains that AI can provide real-time grading and inform patients of a need for referral sooner than human graders. The Conclusions explicitly suggest that instant results, rather than delayed communication of human-grader results, most likely explain higher follow-up uptake. The paper therefore does discuss a possible mechanism, although it does not establish one experimentally. (LLM, conf=0.84)

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  • A recent meta-analysis of four studies by Rahmati et al. showed positive effects of artificial intelligence-assisted diabetic retinopathy screening on referral uptake.

    “A recent meta-analysis of four studies13–15,17 by Rahmati et al.23 showed positive effects of AI-assisted DR screening on referral uptake, but did not explore mechanisms underpinning these differences.”

    From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [23]

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Sorted by citations

Authors
Rahmati, M., Smith, L., Piyasena, M. P., Bowen, M., Boyer, L., Fond, G., Kazemi, A., Yon, D. K., Lee, H., Sehmbi, T., Ahluwalia, S., & Pardhan, S.
Affiliations
  • Aix-Marseille Université (FR)
  • Anglia Ruskin University (GB)
  • College of Optometrists (GB)
  • Vali Asr University of Rafsanjan (IR)
  • Kyung Hee University (KR)
  • Kyung Hee University Medical Center (KR)
Corresponding author
  • Rahmati, Masoud
  • Pardhan, Shahina
ORCID
Venue
Eye
Publisher
Springer Science and Business Media LLC
Published
2025-05-30
Volume
39
Issue
12
Pages
2398-2406
ISSN
  • 0078-5334 (ISSN-L)
  • 0950-222X
  • 1476-5454
DOI
10.1038/s41433-025-03849-4
PMC
PMC12325731
OpenAlex
W4410886022
Cited by
11
References
7
Citations by year
2026: 8 · 2025: 3
FWCI
1.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Eye (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
Vision and Eye Research Institute, School of Medicine, Anglia Ruskin University, Young Street, Cambridge, United Kingdom
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
  • Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
Keywords
artificial intelligence, diabetic retinopathy, diabetes screening, retinal image analysis
MeSH terms
Appointments and Schedules, Artificial Intelligence, Diabetic Retinopathy, Humans

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