CoChat
Sign Up
Cited by 8Retinal Imaging and AnalysisHealth Systems, Economic Evaluations, Quality of LifeRetinal Diseases and Treatments

Health Economic Considerations for the Implementation of Artificial Intelligence‐Enabled Diabetic Retinopathy Screening: A Review

Open access
Leigh, James, Drinkwater, Jocelyn, Turner, Angus, Schroeder, Elizabeth‐Ann·2026·Clinical & Experimental Ophthalmology
DOI 10.1111/ceo.70016PMC PMC12886596
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) has comparable accuracy to ophthalmologists for diabetic retinopathy (DR) screening, yet its cost‐effectiveness is crucial for implementation. Our review of 18 health economic analyses of AI versus manual grading for DR found significant methodological variation, with cost‐utility analysis and Markov modelling being the commonest evaluation and modelling approaches, respectively. We identified three key considerations when appraising health economic analyses of AI‐enabled DR screening: the importance of contextualised parameters including subgroup analysis, real‐world data on adherence to ophthalmology follow‐up, and the trade‐off between diagnostic accuracy and cost‐effectiveness. 39% of studies followed standardised reporting guidelines, and most did not consider improved follow‐up after AI screening, potentially underestimating its economic value. Future evaluations should incorporate contextualised parameters, including adherence and regional data, and recognise that the most accurate diagnostic screening may not reflect the most cost‐effective. Studies should follow updated reporting guidelines such as CHEERS‐AI or PICOTS‐ComTeC to improve methodological transparency.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/ceo.70016

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Economic modelling studies of artificial intelligence diabetic retinopathy screening are limited by a lack of real-world data to inform parameter selection including transition probabilities, utility values, regional prevalence and uptake of screening and follow-up.

    “However, these economic modelling studies are limited by a lack of real-world data to inform parameter selection including transition probabilities, utility values, regional prevalence and uptake of screening and follow-up10.”

    From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [10]

    The Results (Types of Economic Analyses and Modelling) report that transition probabilities often came from other countries, and the quality assessment identifies problems with utility-value sources and the transferability of disease-prevalence assumptions. Results (Implementation and Adherence Considerations) explicitly describe limited real-world data on follow-up adherence. The review does not clearly identify a lack of real-world data on initial screening uptake, so that item in the claim is not supported. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 42

  1. [1]J. W. Y. Yau, S. L. Rogers, R. Kawasaki, et al., “ Global Prevalence and Major Risk Factors of Diabetic Retinopathy,” Diabetes Care 35, no. 3 (2012): 556– 564. 22301125 PMC3322721
  2. [3]M. J. Burton, J. Ramke, A. P. Marques, et al., “ The Lancet Global Health Commission on Global Eye Health: Vision Beyond 2020,” Lancet Global Health 9, no. 4 (2021): e489– e551. 33607016 PMC7966694
  3. [4]D. B. Rein, J. S. Wittenborn, P. Zhang, et al., “ The Economic Burden of Vision Loss and Blindness in the United States,” Ophthalmology 129, no. 4 (2022): 369– 378. 34560128
  4. [5]J. C. Javitt and L. P. Aiello, “ Cost‐Effectiveness of Detecting and Treating Diabetic Retinopathy,” Annals of Internal Medicine 124, no. 1 Pt 2 (1996): 164– 169. 8554212
  5. [6]A. Abou Taha, S. Dinesen, A. S. Vergmann, and J. Grauslund, “ Present and Future Screening Programs for Diabetic Retinopathy: A Narrative Review,” International Journal of Retina and Vitreous 10, no. 1 (2024): 14. 38310265 PMC10838429
  6. [7]M. Rahmati, L. Smith, L. Boyer, et al., “ Factors Affecting Global Adherence for the Uptake of Diabetic Retinopathy Screening: A Systematic Review and Meta‐Analysis,” American Journal of Ophthalmology 268 (2024): 94– 107. 39094991
  7. [8]J. S. Wittenborn, Q. Gu, E. Erdem, et al., “ The Prevalence of Diagnosis of Major Eye Diseases and Their Associated Payments in the Medicare Fee‐For‐Service Program,” Ophthalmic Epidemiology 30, no. 2 (2023): 129– 141. 34530688
  8. [10]J. Lin, J. S. Chang, and W. E. Smiddy, “ Cost Evaluation of Panretinal Photocoagulation Versus Intravitreal Ranibizumab for Proliferative Diabetic Retinopathy,” Ophthalmology 123, no. 9 (2016): 1912– 1918. 27425822 PMC4995116
  9. [11]S. Vujosevic, S. J. Aldington, P. Silva, et al., “ Screening for Diabetic Retinopathy: New Perspectives and Challenges,” Lancet Diabetes & Endocrinology 8, no. 4 (2020): 337– 347. 32113513
  10. [12]K. Widner, S. Virmani, J. Krause, et al., “ Lessons Learned From Translating AI From Development to Deployment in Healthcare,” Nature Medicine 29, no. 6 (2023): 1304– 1306. 37248297
  11. [14]A. A. van der Heijden, M. D. Abramoff, F. Verbraak, M. V. van Hecke, A. Liem, and G. Nijpels, “ Validation of Automated Screening for Referable Diabetic Retinopathy With the IDx‐DR Device in the Hoorn Diabetes Care System,” Acta Ophthalmologica 96, no. 1 (2018): 63– 68. 29178249 PMC5814834
  12. [15]J. I. Lim, C. D. Regillo, S. R. Sadda, et al., “ Artificial Intelligence Detection of Diabetic Retinopathy,” Ophthalmology Science 3, no. 1 (2022): 100228. 36345378 PMC9636573
  13. [18]M. J. Page, J. E. McKenzie, P. M. Bossuyt, et al., “ The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews,” BMJ (Clinical Research ed.) 372 (2021): n71. PMC8005924 33782057
  14. [19]M. Ahmed, T. Dai, R. Channa, M. D. Abramoff, H. P. Lehmann, and R. M. Wolf, “ Cost‐Effectiveness of AI for Pediatric Diabetic Eye Exams From a Health System Perspective,” npj Digital Medicine 8, no. 1 (2025): 3. 39747639 PMC11697205
  15. [20]M. Karabeg, G. Petrovski, S. N. W. Hertzberg, et al., “ A Pilot Cost‐Analysis Study Comparing AI‐Based EyeArt and Ophthalmologist Assessment of Diabetic Retinopathy in Minority Women in Oslo, Norway,” International Journal of Retina and Vitreous 10, no. 1 (2024): 40. 38783384 PMC11112837
  16. [22]W. Hu, Z. Zhu, M. He, and L. Zhang, “ Cost‐Effectiveness Analysis of Artificial Intelligence‐Based Diabetic Retinopathy Screening in Australian Primary Care Settings,” Investigative Ophthalmology & Visual Science 65, no. 7 (2024): 335.
  17. [24]A. Srisubat, K. Kittrongsiri, S. Sangroongruangsri, et al., “ Cost‐Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program,” Ophthalmology and therapy 12, no. 2 (2023): 1339– 1357. 36841895 PMC10011252
  18. [26]H. Liu, R. Li, Y. Zhang, et al., “ Economic Evaluation of Combined Population‐Based Screening for Multiple Blindness‐Causing Eye Diseases in China: Aa Cost‐Effectiveness Analysis,” Lancet Global Health 11, no. 3 (2023): e456– e465. 36702141
  19. [28]A. P. O. Rosses, Â. J. Ben, C. F. de Souza, et al., “ Diagnostic Performance of Retinal Digital Photography for Diabetic Retinopathy Screening in Primary Care,” Family Practice 34, no. 5 (2017): 546– 551. 28379410
  20. [30]S. D. Fuller, J. Hu, J. C. Liu, et al., “ Five‐Year Cost‐Effectiveness Modeling of Primary Care‐Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low‐Income Patients With Diabetes,” Journal of Diabetes Science and Technology 16, no. 2 (2022): 415– 427. 33124449 PMC8861785
  21. [31]Y. Xie, Q. D. Nguyen, H. Hamzah, et al., “ Artificial Intelligence for Teleophthalmology‐Based Diabetic Retinopathy Screening in a National Programme: an Economic Analysis Modelling Study,” Lancet Digital Health 2, no. 5 (2020): e240– e249. 33328056
  22. [33]R. M. Wolf, R. Channa, M. D. Abramoff, and H. P. Lehmann, “ Cost‐Effectiveness of Autonomous Point‐Of‐Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes,” JAMA Ophthalmology 138, no. 10 (2020): 1063– 1069. 32880616 PMC7489415
  23. [34]A. Tufail, C. Rudisill, C. Egan, et al., “ Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost‐Effectiveness Compared With Human Graders,” Ophthalmology 124, no. 3 (2017): 343– 351. 28024825
  24. [35]G. Prescott, P. Sharp, K. Goatman, et al., “ Improving the Cost‐Effectiveness of Photographic Screening for Diabetic Macular Oedema: A Prospective, Multi‐Centre, UK Study,” British Journal of Ophthalmology 98, no. 8 (2014): 1042– 1049. 24682180
  25. [36]G. S. Scotland, P. McNamee, A. D. Fleming, et al., “ Costs and Consequences of Automated Algorithms Versus Manual Grading for the Detection of Referable Diabetic Retinopathy,” British Journal of Ophthalmology 94, no. 6 (2010): 712– 719. 19965826
  26. [37]G. S. Scotland, P. McNamee, S. Philip, et al., “ Cost‐Effectiveness of Implementing Automated Grading Within the National Screening Programme for Diabetic Retinopathy in Scotland,” British Journal of Ophthalmology 91, no. 11 (2007): 1518– 1523. 17585001 PMC2095413
  27. [38]G. S. Scotland, P. McNamee, A. D. Fleming, et al., “ Costs and Consequences of Automated Algorithms Versus Manual Grading for the Detection of Referable Diabetic Retinopathy,” British Journal of Ophthalmology 94, no. 6 (2010): 712– 719. 19965826
  28. [39]N. De Lew, G. Greenberg, and K. Kinchen, “ A Layman's Guide to the U.S. Health Care System,” Health Care Financing Review 14, no. 1 (1992): 151– 169. PMC4193322 10124436
  29. [40]T. Das, B. Takkar, S. Sivaprasad, et al., “ Recently Updated Global Diabetic Retinopathy Screening Guidelines: Commonalities, Differences, and Future Possibilities,” Eye (London, England) 35, no. 10 (2021): 2685– 2698. 33976399 PMC8452707
  30. [41]A. Briggs and M. Sculpher, “ An Introduction to Markov Modelling for Economic Evaluation,” PharmacoEconomics 13, no. 4 (1998): 397– 409. 10178664
  31. [42]T. H. Tung, S.‐J. Chen, H.‐C. Shih, et al., “ Assessing the Natural Course of Diabetic Retinopathy: A Population‐Based Study in Kinmen, Taiwan,” Ophthalmic Epidemiology 13, no. 5 (2006): 327– 333. 17060111
  32. [43]J. Lian, S. M. McGhee, R. A. Gangwani, et al., “ The Impact of a Co‐Payment on the Cost‐Effectiveness of Screening for Diabetic Retinopathy,” Journal of Public Health 38, no. 4 (2016): 782– 792. 28158645
  33. [44]S. Vijan, T. P. Hofer, and R. A. Hayward, “ Cost‐Utility Analysis of Screening Intervals for Diabetic Retinopathy in Patients With Type 2 Diabetes Mellitus,” Journal of the American Medical Association 283, no. 7 (2000): 889– 896. 10685713
  34. [45]D. J. McCarty, C. L. Fu, C. A. Harper, H. R. Taylor, and C. A. McCarty, “ Five‐Year Incidence of Diabetic Retinopathy in the Melbourne Visual Impairment Project,” Clinical & Experimental Ophthalmology 31, no. 5 (2003): 397– 402. 14516426
  35. [46]P. Royle, H. Mistry, P. Auguste, et al., “ Pan‐Retinal Photocoagulation and Other Forms of Laser Treatment and Drug Therapies for Non‐Proliferative Diabetic Retinopathy: Systematic Review and Economic Evaluation,” Health Technology Assessment 19, no. 51 (2015): 1– 247. PMC4780877 26173799
  36. [47]N. Aoki, K. Dunn, T. Fukui, J. R. Beck, W. J. Schull, and H. K. Li, “ Cost‐Effectiveness Analysis of Telemedicine to Evaluate Diabetic Retinopathy in a Prison Population,” Diabetes Care 27, no. 5 (2004): 1095– 1101. 15111527
  37. [50]W. Mathenge, N. Whitestone, J. Nkurikiye, et al., “ Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low‐Resource Setting: The RAIDERS Randomized Trial,” Ophthalmology Science 2, no. 4 (2022): 100168. 36531575 PMC9754978
  38. [52]J. Elvidge, C. Hawksworth, T. S. Avşar, et al., “ Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS‐AI),” Value in Health 27, no. 9 (2024): 1196– 1205. 38795956 PMC11343728
  39. [53]Z. Zrubka, A. Champion, A. P. Holtorf, et al., “ The PICOTS‐ComTeC Framework for Defining Digital Health Interventions: An ISPOR Special Interest Group Report,” Value in Health 27, no. 4 (2024): 383– 396. 38569772
  40. [54]E. P. Mseke, B. Jessup, and T. Barnett, “ Impact of Distance and/or Travel Time on Healthcare Service Access in Rural and Remote Areas: A Scoping Review,” Journal of Transport & Health 37 (2024): 101819.
  41. [55]D. J. Torgerson and D. M. Reid, “ Estimating the Cost Effectiveness of Screening Tests,” QJM: An International Journal of Medicine 90, no. 10 (1997): 649– 652. 9415348
  42. [56]J. Shreffler and M. R. Huecker, “ Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios,” in StatPearls [Internet] (StatPearls Publishing, 2025), [cited 2025 Apr 2], http://www.ncbi.nlm.nih.gov/books/NBK557491/. 32491423

Cited by

Sorted by citations

Authors
Leigh, J., Drinkwater, J., Turner, A., & Schroeder, E.
Affiliations
  • Lions Eye Institute (AU)
  • Nuffield Department of Primary Care Health Sciences University of Oxford UK
Corresponding author
Leigh, James
ORCID
Venue
Clinical & Experimental Ophthalmology
Publisher
Wiley
Published
2025-11-03
Volume
54
Issue
1
Pages
144-161
ISSN
  • 1442-6404 (ISSN-L)
  • 1442-9071
DOI
10.1111/ceo.70016
PMC
PMC12886596
OpenAlex
W4415834977
Cited by
8
References
12
Citations by year
2026: 7 · 2025: 1
FWCI
1.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinical and Experimental Ophthalmology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
Funders
University of Oxford
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Keywords
diabetic retinopathy, artificial intelligence, cost-effectiveness, health economic evaluation, follow-up adherence, subgroup analysis, reporting guidelines, diagnostic accuracy
MeSH terms
Artificial Intelligence, Cost-Benefit Analysis, Diabetic Retinopathy, Humans, Mass Screening, Health Care Costs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review