AUTOMATED AI SCREENING FOR DIABETIC RETINOPATHY: A SYSTEMATIC REVIEW OF SOCIO-ECONOMIC ACCESSIBILITY, PATIENT ACCEPTANCE, AND THE DIGITAL DIVIDE
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.5728Keywords:
Diabetic Retinopathy, Autonomous Artificial Intelligence, Socio-Technical Integration, Health Equity, Patient Acceptance, Cost-EffectivenessAbstract
Purpose: This systematic review evaluates the socio-technical integration of autonomous artificial intelligence (AI) in diabetic retinopathy (DR) screening. While the technical accuracy of deep learning algorithms is well-established, their successful deployment depends on a complex interplay of economic, psychological, and organizational factors. This study synthesizes evidence from 35 peer-reviewed sources to provide a comprehensive roadmap for AI implementation in diverse healthcare settings.
Methods: A systematic analysis of 35 high-quality studies (2017–2026) was conducted, focusing on diagnostic performance, cost-effectiveness, and stakeholder acceptance. The findings were interpreted through the Consolidated Framework for Implementation Research (CFIR) to identify systemic barriers and facilitators.
Results: The evidence confirms that autonomous AI reaches high sensitivity (95.7%–100%) in detecting referable DR, matching specialist performance. Economically, AI is highly cost-effective in resource-limited and rural areas by reducing travel costs and labor burdens. However, significant barriers remain, including the "biological divide" in elderly populations, "black box" anxiety among patients, and organizational disruption in primary care workflows. Trust is identified as a critical mediator, with acceptance increasing when AI is positioned as a supportive "safety net" rather than a human replacement.
Conclusion: The transition to AI-driven screening is a transformative shift toward democratized healthcare. Success requires a move toward Human-Centered Design (HCD), localized staff education using tools like instructional videos, and inclusive governance to bridge the digital divide.
References
Bai, B., Liu, X., & Li, H. (2026). Federated multimodal AI for precision-equitable diabetes care. Frontiers in Digital Health, 7, Article 1678047. https://doi.org/10.3389/fdgth.2025.1678047
Chen, Y., Song, F., Zhao, Z., Wang, Y., Shi, E. D., Chen, X., Xu, L., Shang, X., Lai, M., Liu, Y., & He, M. (2025). Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost portable fundus camera for diabetic retinopathy screening in primary health care settings. Diabetes Research and Clinical Practice, 223, Article 112161. https://doi.org/10.1016/j.diabres.2025.112161
Cleland, C. R., Rwiza, J., Evans, J. R., Gordon, I., MacLeod, D., Burton, M. J., & Bascaran, C. (2023). Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: A scoping review. BMJ Open Diabetes Research & Care, 11(1), Article e003424. https://doi.org/10.1136/bmjdrc-2023-003424
Crew, A., Reidy, C., van der Westhuizen, H.-M., & Graham, M. (2024). A narrative review of ethical issues in the use of artificial intelligence enabled diagnostics for diabetic retinopathy. Journal of Evaluation in Clinical Practice. Advance online publication. https://doi.org/10.1111/jep.14237
Cuadros, J. (2021). The real-world impact of artificial intelligence on diabetic retinopathy screening in primary care. Journal of Diabetes Science and Technology, 15(3), 664–665. https://doi.org/10.1177/1932296820914287
Dave, D., Steinhubl, S. R., & McQueen, R. B. (2026). Deep learning-enabled diabetic retinopathy screening: A techno-clinical revolution or just more artificial intelligence hype? Diabetes Care, 49, 381–383. https://doi.org/10.2337/dci25-0123
Goldstein, J., Weitzman, D., Lemerond, M., & Jones, A. (2023). Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: Key best practices learned through collection of real-world data. Frontiers in Digital Health, 5, Article 1004130. https://doi.org/10.3389/fdgth.2023.1004130
Held, L. A., Wewetzer, L., & Steinhäuser, J. (2022). Determinants of the implementation of an artificial intelligence-supported device for the screening of diabetic retinopathy in primary care: A qualitative study. Health Informatics Journal, 28(3). https://doi.org/10.1177/14604582221112816
Heydon, P., Egan, C., Bolter, L., Chambers, R., Anderson, J., Aldington, S., Stratton, I. M., Scanlon, P. H., Webster, L., Mann, S., du Chemin, A., Owen, C. G., Tufail, A., & Rudnicka, A. R. (2021). Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30,000 patients. British Journal of Ophthalmology, 105(5), 723–728. https://doi.org/10.1136/bjophthalmol-2020-316594
Hu, W., Joseph, S., Li, R., Woods, E., Sun, J., Shen, M., Jan, C. L., Zhu, Z., He, M., & Zhang, L. (2024). Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: A cost-effectiveness analysis. eClinicalMedicine, 67, Article 102387. https://doi.org/10.1016/j.eclinm.2023.102387
Jin, L., Tao, Y., Liu, Y., Liu, G., Lin, L., Chen, Z., & Peng, S. (2025). SEM model analysis of diabetic patients’ acceptance of artificial intelligence for diabetic retinopathy. BMC Medical Informatics and Decision Making, 25, Article 175. https://doi.org/10.1186/s12911-025-03008-5
Kawasaki, R. (2024). How can artificial intelligence be implemented effectively in diabetic retinopathy screening in Japan? Medicina, 60(2), Article 243. https://doi.org/10.3390/medicina60020243
Keel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J., & He, M. (2018). Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: A pilot study. Scientific Reports, 8(1), Article 4330. https://doi.org/10.1038/s41598-018-22612-2
Krogh, M., Germund Nielsen, M., Byskov Petersen, G., Jensen, M. S. A., Jensen, M. B., Vorum, H., Bruun, N. H., & Kolding Kristensen, J. (2025a). Patient acceptance of AI-assisted diabetic retinopathy screening in primary care: Findings from a questionnaire-based feasibility study. Frontiers in Medicine, 12, Article 1610114. https://doi.org/10.3389/fmed.2025.1610114
Krogh, M., Hentze, M., Jensen, M. S. A., Jensen, M. B., Nielsen, M. G., Vorum, H., & Kolding Kristensen, J. (2025b). Valuable insights into general practice staff’s experiences and perspectives on AI-assisted diabetic retinopathy screening: An interview study. Frontiers in Medicine, 12, Article 1565532. https://doi.org/10.3389/fmed.2025.1565532
Krogh, M., Jensen, M. B., Jensen, M. S. A., Hansen, M. H., Nielsen, M. G., Vorum, H., & Kolding Kristensen, J. (2025c). Exploring general practice staff perspectives on a teaching concept based on instruction videos for diabetic retinopathy screening: An interview study. Scandinavian Journal of Primary Health Care, 43(1), 75–84. https://doi.org/10.1080/02813432.2024.2396873
Leigh, J., Drinkwater, J., Turner, A., & Schroeder, E.-A. (2026). Health economic considerations for the implementation of artificial intelligence-enabled diabetic retinopathy screening: A review. Clinical & Experimental Ophthalmology, 54, 144–161. https://doi.org/10.1111/ceo.70016
Li, H., Li, G., Li, N., Liu, C., Yuan, Z., Gao, Q., Hao, S., Fan, S., & Yang, J. (2023). Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model. PLOS ONE, 18(11), Article e0291390. https://doi.org/10.1371/journal.pone.0291390
Liao, X., Yao, C., Jin, F., Zhang, J., & Liu, L. (2024). Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: A qualitative study using the updated Consolidated Framework for Implementation Research. BMJ Open, 14(10), Article e084398. https://doi.org/10.1136/bmjopen-2024-084398
Lin, S., Ma, Y., Xu, Y., Lu, L., He, J., Zhu, J., Peng, Y., Yu, T., Congdon, N., & Zou, H. (2023). Artificial intelligence in community-based diabetic retinopathy telemedicine screening in urban China: Cost-effectiveness and cost-utility analyses with real-world data. JMIR Public Health and Surveillance, 9, Article e41624. https://doi.org/10.2196/41624
Liu, T. Y. A., Huang, J., Channa, R., Wolf, R., Dong, Y., Liang, M., Wang, J., & Abramoff, M. (2024). Autonomous artificial intelligence increases access and health equity in underserved populations with diabetes. Research Square. https://doi.org/10.21203/rs.3.rs-3979992/v1
Macdonald, T., Zhelev, Z., Liu, X., Hyde, C., Fajtl, J., Egan, C., Tufail, A., Rudnicka, A. R., Shinkins, B., Given-Wilson, R., Dunbar, J. K., Halligan, S., Scanlon, P., Mackie, A., Taylor-Phillips, S., & Denniston, A. K. (2025). Generating evidence to support the role of AI in diabetic eye screening: Considerations from the UK National Screening Committee. The Lancet Digital Health, 7(1), e84–e90. https://doi.org/10.1016/j.landig.2024.12.004
Mathenge, W., Whitestone, N., Nkurikiye, J., Patnaik, J. L., Piyasena, P., Uwaliraye, P., Lanouette, G., Kahook, M. Y., Cherwek, D. H., Congdon, N., & Jaccard, N. (2022). Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: The RAIDERS randomized trial. Ophthalmology Science, 2(4), Article 100168. https://doi.org/10.1016/j.xops.2022.100168
Mo, Y., Zhao, F., Yuan, L., Xing, Q., Zhou, Y., Wu, Q., Li, C., Lin, J., Wu, H., Deng, S., & Zhang, M. (2025). Healthcare providers’ perceptions of artificial intelligence in diabetes care: A cross-sectional study in China. International Journal of Nursing Sciences, 12, 218–224. https://doi.org/10.1016/j.ijnss.2025.04.013
Nolan, B., Daybranch, E. R., Barton, K., & Korsen, N. (2023). Patient and provider experience with artificial intelligence screening technology for diabetic retinopathy in rural primary care setting. Journal of Maine Medical Center, 5(2), Article 6. https://doi.org/10.46804/2641-2225.1144
Rajesh, A. E., Davidson, O. Q., Lee, C. S., & Lee, A. Y. (2023). Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness. Diabetes Care, 46(10), 1728–1739. https://doi.org/10.2337/dci23-0032
Rustam, Z., Xie, Y., Moreno, J. A., Tran, D., Zhu, G., Yu, S. E., Sandrosyan, A., & Cai, C. X. (2026). Patient perspectives on artificial intelligence-based diabetic retinopathy screening at an urban US medical center. Clinical Ophthalmology, 20, Article 581564. https://doi.org/10.2147/opth.s581564
Scanzera, A. C., Beversluis, C., Potharazu, A. V., Bai, P., Leifer, A., Cole, E., Du, D. Y., Musick, H., & Chan, R. V. P. (2023). Planning an artificial intelligence diabetic retinopathy screening program: A human-centered design approach. Frontiers in Medicine, 10, Article 1198228. https://doi.org/10.3389/fmed.2023.1198228
Shahzad, R., Mehmood, A., Shabbir, D., & Siddiqui, M. A. R. (2024). Diagnostic accuracy of a smartphone-based device (VistaView) for detection of diabetic retinopathy: A prospective study. PLOS Digital Health, 3(11), Article e0000649. https://doi.org/10.1371/journal.pdig.0000649
Teng, C. W., Patel, S. D., Barkmeier, A. J., Liu, T. Y. A., Myung, D., Henderer, J., Liu, J., Hansen, E., & Al-Aswad, L. A. (2025). Autonomous artificial intelligence in diabetic retinopathy testing: Lessons learned on successful health system adoption. Ophthalmology. Advance online publication. https://doi.org/10.1016/j.xops.2025.100935
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., Bolter, L., Srinivas, S., Nittala, M., Sadda, S., Taylor, P., & Rudnicka, A. R. (2017). Automated diabetic retinopathy image assessment software: Diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology, 124(3), 343–351. https://doi.org/10.1016/j.ophtha.2016.11.014
Wahlich, C., Chandrasekaran, L., Chaudhry, U. A. R., Willis, K., Chambers, R., Bolter, L., Anderson, J., Shakespeare, R., Olvera-Barrios, A., Fajtl, J., Welikala, R., Barman, S., Egan, C. A., Tufail, A., Owen, C. G., & Rudnicka, A. R. (2025). Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme. Diabetes Research and Clinical Practice, 219, Article 111964. https://doi.org/10.1016/j.diabres.2024.111964
Wang, Y., Liu, C., Hu, W., Luo, L., Shi, D., Zhang, J., Yin, Q., Zhang, L., Han, X., & He, M. (2024). Economic evaluation for medical artificial intelligence: Accuracy vs. cost-effectiveness in a diabetic retinopathy screening case. npj Digital Medicine, 7, Article 43. https://doi.org/10.1038/s41746-024-01032-9
Wewetzer, L., Held, L. A., & Steinhäuser, J. (2021). Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care: A meta-analysis. PLOS ONE, 16(8), Article e0255034. https://doi.org/10.1371/journal.pone.0255034
Yap, A., Wilkinson, B., Chen, E., Han, L., Vaghefi, E., Galloway, C., & Squirrell, D. (2022). Patients perceptions of artificial intelligence in diabetic eye screening. Asia-Pacific Journal of Ophthalmology, 11(3), 287–293. https://doi.org/10.1097/APO.0000000000000525
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Copyright (c) 2026 Wiktoria Donocik, Jakub Sałak, Piotr Tryczyński, Jakub Wrona, Piotr Helbin, Aleksandra Gralec, Sebastian Ożga, Aleksandra Spirkowicz
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