Feasibility and Patient Experience of a Pilot Artificial Intelligence-Based Diabetic Retinopathy Screening Program in Northern Ontario
Open accessAbstract
Purpose To assess the feasibility, implementation, and patient experience of autonomous artificial intelligence-based diabetic retinopathy detection models.Methods This was a prospective cohort study where consenting adult participants previously diagnosed with diabetes were screened for diabetic retinopathy using retinal imaging with autonomous artificial intelligence (AI) interpretation at their routine primary care appointment from December 2022 through October 2023 in Thunder Bay, Ontario. Demographic (age, sex, race) and clinical (type and duration of diabetes, last reported eye exam) data were collected using a data collection form. A 5-point Likert scale questionnaire was completed by participants to assess patient experience following the AI exam.Results Among the 202 participants (38.6% women) with a mean age of 70.8 ± 11.7 years included in the study and screened by AI, the exam was successfully completed by 93.6% (n = 189), with only 1.5% (n = 3) requiring dilating eyedrops. The most common reason for an unsuccessful exam was small pupils with patient refusal for dilating eyedrops (n = 4). Among the participants with successful eye exams, 22.2% (n = 42) had referable diabetic retinopathy detected and were referred to see an ophthalmologist; 32/42 (76.0%) of these attended their ophthalmologist appointment. A total of 184 participants completed the satisfaction questionnaire; the mean score (out of 5) for satisfaction with the addition of an eye exam to their primary care visit was 4.8 ± 0.6.Conclusion Screening for diabetic retinopathy using autonomous artificial intelligence in a primary care setting is feasible and acceptable. This approach has significant advantages for both physicians and patients while achieving very high patient satisfaction.
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.
Integrity signals
Registry
Cites
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.
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?
Patients like not having to wait or come in for an additional specialist visit and not having to use dilating eyedrops with its consequences on their productivity for the next few hours.
“Patients like not having to wait or come in for an additional specialist visit and not having to use dilating eyedrops with its consequences on their productivity for the next few hours.6”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [6]
The Results report that participants highly valued not waiting for a separate eye-exam visit and not using dilating drops; the Discussion links both to satisfaction. The paper also says some participants refused drops because they needed to drive home, but it does not report an effect on productivity over the next few hours. No page numbers are available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAI-enabled screening may be particularly valuable for rural and remote populations, low-income patients, older adults, and people who face long travel distances or limited specialist availability.
“It may be particularly valuable for rural and remote populations, lowincome patients, older adults, and people who face long travel distances or limited specialist availability (28)(29)(30).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [29]
The Introduction describes rural and remote areas of Northwestern Ontario as facing long travel distances and physician shortages, and the Discussion says AI screening may improve access where eye-care services are limited. The Results report a mean participant age of 70.8 years, but the paper does not establish that screening is particularly valuable for older adults or address low-income patients as a group. It also calls for further study in more remote and rural settings. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe AI-based approach has high levels of patient satisfaction.
“Given the cost per case advantage of the AI-based approach, combined with its feasibility and high levels of patient satisfaction, this suggests significant advantages to the implementation of AI-based screening for DR for both physicians and patients.”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [6]
The AI-based approach is feasible.
“Given the cost per case advantage of the AI-based approach, combined with its feasibility and high levels of patient satisfaction, this suggests significant advantages to the implementation of AI-based screening for DR for both physicians and patients.”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [6]
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
Artificial intelligence for diabetic retinopathy screening: a review
Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W. (2020) · Eye · doi:10.1038/s41433-019-0566-0 · Cited by 376 · see citing works
Ipp, E., Liljenquist, D., Bode, B., Shah, V. N., Silverstein, S., Regillo, C. D., Lim, J. I., Sadda, S., Domalpally, A., Gray, G., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.34254 · Cited by 256 · see citing works
Bhaskaranand, M., Ramachandra, C., Bhat, S., Cuadros, J., Nittala, M. G., Sadda, S. R., & Solanki, K. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0164 · Cited by 211 · see citing works
Wolf, R. M., Channa, R., Liu, T. Y. A., Zehra, A., Bromberger, L., Patel, D., Ananthakrishnan, A., Brown, E. A., Prichett, L., Lehmann, H. P., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44676-z · Cited by 116 · see citing works
Flagged first, then by citations
Research works printed but not linked: 28
- Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous ai-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):39. doi:10.1038/s41746-018-0040-6. · DOI · Paper page
- Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–351. doi:10.1016/j.ophtha.2016.11.014. · DOI · Paper page
- Sarao V, Veritti D, Lanzetta P. Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study. Graefes Arch Clin Exp Ophthalmol. 2020;258(12):2647–2654. doi:10.1007/s00417-020-04853-y. · DOI
- Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2021;105(5):723–728. doi:10.1136/bjophthalmol-2020-316594. · DOI
- Mokhashi N, Grachevskaya J, Cheng L, Yu D, Lu X, Zhang Y, et al. A comparison of artificial intelligence and human diabetic retinal image interpretation in an Urban health system. J Diabetes Sci Technol. 2022;16(4):1003–1007. doi:10.1177/1932296821999370. · DOI · Paper page
- Lim JI, Regillo CD, Sadda SR, Ipp E, Bhaskaranand M, Ramachandra C, et al. Artificial intelligence detection of diabetic retinopathy: subgroup comparison of the EyeArt system with ophthalmologists’ dilated examinations. Ophthalmol Sci. 2022;3(1):100228. doi:10.1016/j.xops.2022.100228. · DOI · Paper page
- Abramoff MD, Whitestone N, Patnaik JL, Rich E, Ahmed M, Husain L, et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. NPJ Digit Med. 2023;6(1):184. doi:10.1038/s41746-023-00931-7. · DOI · Paper page
- Turin TC, Saad N, Jun M, Tonelli M, Ma Z, Barnabe CCM, et al. Lifetime risk of diabetes among first nations and non–first nations people. CMAJ. 2016;188(16):1147–1153. doi:10.1503/cmaj.150787. · DOI
- International Diabetes Federation. IDF diabetes atlas 10th edition. https://diabetesatlas.org/atlas/tenth-edition/(open in a new window). 2021. Accessed February 27, 2024.
- Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–564. doi:10.2337/dc11-1909. · DOI · Paper page
- Ross SA, McKenna A, Mozejko S, Fick GH. Diabetic retinopathy in native and nonnative canadians. Exp Diabetes Res. 2007;2007(1):76271. doi:10.1155/2007/76271. · DOI
- Diabetes Canada Clinical Practice Guidelines Expert Committee, Altomare F, Kherani A, Lovshin J. Retinopathy. Can J Diabetes. 2018;42(Suppl1):S210–6. doi:10.1016/j.jcjd.2017.10.027. · DOI
- Javitt JC, Aiello LP. Cost-effectiveness of detecting and treating diabetic retinopathy. Ann Intern Med. 1996;124(1 Pt 2):164–169. doi:10.7326/0003-4819-124-1_part_2-199601011-00017. · DOI · Paper page
- Tennant MT, Rudnisky CJ, Johnson JA. Diabetes and eye disease in Alberta. In: Johnson J, ed. Alberta Diabetes. Vol. 1. atlas. 1st ed. Edmonton: Alberta Health and Wellness; 2007:153–174.
- Tennant MT, Rudnisky CJ, Hinz BJ, MacDonald IM, Greve MDJ. Tele-ophthalmology via stereoscopic digital imaging: a pilot project. Diabetes Technol Ther. 2000;2(4):583–587. doi:10.1089/15209150050502005. · DOI
- Pong RW. Strategies to overcome physician shortages in northern Ontario: a study of policy implementation over 35 years. Hum Resour Health. 2008;6(1):24. doi:10.1186/1478-4491-6-24. · DOI
- Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018;8(1):4330. doi:10.1038/s41598-018-22612-2. · DOI · Paper page
- Scheetz J, Koca D, McGuinness M, Holloway E, Tan Z, Zhu Z, et al. Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Sci Rep. 2021;11(1):15808. doi:10.1038/s41598-021-94178-5. · DOI · Paper page
- Whitestone N, Nkurikiye J, Patnaik JL, Jaccard N, Lanouette G, Cherwek DH, et al. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. Br J Ophthalmol. 2023;108(6):840–845. doi:10.1136/bjo-2022-322683. · DOI · Paper page
- Lawrenson JG, Graham-Rowe E, Lorencatto F, Burr J, Bunce C, Francis JJ, et al. Interventions to increase attendance for diabetic retinopathy screening. Cochrane Database Syst Rev. 2018;(1). doi:10.1002/14651858.CD012054.pub2. · DOI
- Chew HSJ, Achananuparp P. Perceptions and needs of artificial intelligence in health care to increase adoption: scoping review. J Med Internet Res. 2022;24(1):e32939. doi:10.2196/32939. · DOI · Paper page
- Singareddy S, Sn VP, Jaramillo AP, Yasir M, Iyer N, Hussein S, et al. Artificial intelligence and its role in the management of chronic medical conditions: a systematic review. Cureus. 2023;15(9):e46066. doi:10.7759/cureus.46066. · DOI
- Hooper P, Boucher MC, Cruess A, Dawson KG, Delpero W, Greve M, et al. Canadian ophthalmological society evidence-based clinical practice guidelines for the management of diabetic retinopathy. Can J Ophthalmol. 2012;47(2):S1–S54. doi:10.1016/j.jcjo.2011.12.025. · DOI
- Felfeli T, Katsnelson G, Kiss A, Plumptre L, Paterson JM, Ballios BG, et al. Prevalence and predictors for being unscreened for diabetic retinopathy: a population-based study over a decade. Can J Ophthalmol. 2023;58(4):278–286. doi:10.1016/j.jcjo.2022.04.002. · DOI
- Ballios BG, Park T, Chaudhary V, Hurley B, Kosar S, Sheidow T, et al. Identifying gaps in patient access to diabetic screening eye examinations in Ontario: a provincially representative cross-sectional study. Can J Ophthalmol. 2021;56(4):223–230. doi:10.1016/j.jcjo.2020.10.018. · DOI
- Nguyen M, Stamenova V, Onabajo N, Merritt R, Sutakovic O, Mossman K, et al. Perceptions of a teleophthalmology screening program for diabetic retinopathy in adults with type 1 and type 2 diabetes in urban primary care settings. Can J Diabetes. 2022;46(7):S1499–2671(22)00043–0. doi:10.1016/j.jcjd.2022.03.001. · DOI
- Mathenge W, Whitestone N, Nkurikiye J, Patnaik JL, Piyasena P, Uwaliraye P, et al. Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: the RAIDERS randomized trial. Ophthalmol Sci. 2022;2(4):100168. doi:10.1016/j.xops.2022.100168. · DOI · Paper page
- Duong D, Vogel L. National survey highlights worsening primary care access. CMAJ. 2023;195(16):E592–E593. doi:10.1503/cmaj.1096049. · DOI
Cited by
Sorted by citations
Leigh, J. A., Sherrington, A., Barber, A. R. J., Turner, A. W., Kidd, M., Powell, J., & Pope, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02616-3 · Cited by 2 · see citing works
Chen, J., & Chen, B. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1976572
Di Matteo, L., Whitestone, N., & Bhambhwani, V. (2026) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2026-002904
“6 The results are paralleled in the modified base case that includes the retinal OCT screening, with the AI-based cost per diagnosed case being 53% lower than the traditional physician-based approach.”
Self-citation
- Authors
- Bhambhwani, V., Whitestone, N., Patnaik, J. L., Ojeda, A., Scali, J., & Cherwek, D. H.
- Affiliations
- Thunder Bay Regional Health Sciences Centre (CA)
- Orbis International
- University of Colorado Anschutz (US)
- Clinical Services, Port Arthur Health Centre
- Corresponding author
- Bhambhwani, Vishaal
- Venue
- Ophthalmic Epidemiology
- Publisher
- Informa UK Limited
- Published
- 2024-12-18
- Volume
- 32
- Issue
- 5
- Pages
- 518-524
- ISSN
- 0928-6586 (ISSN-L)
- 1744-5086
- OpenAlex
- W4405519411
- Cited by
- 11
- References
- 4
- Citations by year
- 2026: 6 · 2025: 5
- FWCI
- 2.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ophthalmic Epidemiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Northern Ontario Academic Medicine Association
- Innovation Fund (A-23-02)
- 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, retinal imaging, primary care, patient experience, patient satisfaction, feasibility, referable diabetic retinopathy, dilated eye examination, Northern Ontario
- MeSH terms
- Aged, Aged, 80 and over, Artificial Intelligence, Diabetic Retinopathy, Feasibility Studies, Female, Humans, Male, Mass Screening, Middle Aged, Ontario, Pilot Projects, Prospective Studies, Surveys and Questionnaires, Patient Satisfaction
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
