Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China
Open accessAbstract
Background: Diabetic retinopathy (DR) has become a leading cause of global blindness as a microvascular complication of diabetes. Regular screening of diabetic retinopathy is strongly recommended for people with diabetes so that timely treatment can be provided to reduce the incidence of visual impairment. However, DR screening is not well carried out due to lack of eye care facilities, especially in the rural areas of China. Artificial intelligence (AI) based DR screening has emerged as a novel strategy and show promising diagnostic performance in sensitivity and specificity, relieving the pressure of the shortage of facilities and ophthalmologists because of its quick and accurate diagnosis. In this study, we estimated the cost-effectiveness of AI screening for DR in rural China based on Markov model, providing evidence for extending use of AI screening for DR. Methods: We estimated the cost-effectiveness of AI screening and compared it with ophthalmologist screening in which fundus images are evaluated by ophthalmologists. We developed a Markov model-based hybrid decision tree to analyze the costs, effectiveness and incremental cost-effectiveness ratio (ICER) of AI screening strategies relative to no screening strategies and ophthalmologist screening strategies (dominated) over 35 years (mean life expectancy of diabetes patients in rural China). The analysis was conducted from the health system perspective (included direct medical costs) and societal perspective (included medical and nonmedical costs). Effectiveness was analyzed with quality-adjusted life years (QALYs). The robustness of results was estimated by performing one-way sensitivity analysis and probabilistic analysis. Results: From the health system perspective, AI screening and ophthalmologist screening had incremental costs of $180.19 and $215.05 but more quality-adjusted life years (QALYs) compared with no screening. AI screening had an ICER of $1,107.63. From the societal perspective which considers all direct and indirect costs, AI screening had an ICER of $10,347.12 compared with no screening, below the cost-effective threshold (1–3 times per capita GDP of Chinese in 2019). Conclusions: Our analysis demonstrates that AI-based screening is more cost-effective compared with conventional ophthalmologist screening and holds great promise to be an alternative approach for DR screening in the rural area of China.
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?
The transition rate from non-referable DR to referable DR in China was estimated as 7%.
“To capture the general progression rate of DR in China, we estimated the transition rates from non-referable DR to referable DR as 7%.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [37]
Table 1 (Methods, Model inputs; page unavailable) assigns 7% to progression from no DR to mild DR, not from non-referable to referable DR. It lists 19% for mild-to-moderate DR and 17% for moderate-to-vision-threatening DR. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredXie et al. and Huang et al. found that AI-driven models for diabetic retinopathy screening reduced per-patient screening costs by 14-19.5% and achieved ICERs as low as $1107.63 per QALY.
“both Xie et al.23 and Huang et al.36 found that AI-driven models reduced per-patient screening costs by 14–19.5% and achieved ICERs as low as $1107.63 per QALY.”
From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as Huang et al.36
The Results (Cost-effectiveness analysis; page unavailable) report an ICER of $1,107.63 per QALY for AI screening versus no screening. The Discussion reports lower costs than ophthalmologist screening, but Huang et al. do not report a 19.5% reduction in per-patient screening costs; the 14.3% saving mentioned there is attributed to Xie et al. (LLM, conf=0.90)
Experimental — accuracy not yet measuredXie et al. and Huang et al. showed that performance differences between semi-automated and fully automated AI models directly impacted cost per patient and ICERs.
“the performance differences between semi-automated and fully automated AI models—as shown by Xie et al.23 and Huang et al.36 —directly impacted cost per patient and ICERs”
From Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare (2025) · cites this paper as Huang et al.36
The Discussion (page unavailable) reports lower per-patient grading costs for AI than for ophthalmologist screening, and the Results > Cost-effectiveness analysis reports an ICER for AI screening. The paper does not compare semi-automated with fully automated AI models or show that performance differences between those models directly affected costs or ICERs; it mentions those model types separately when discussing other studies. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost previous health economic studies in China leaned towards adopting AI models with a high specificity (over 97%).
“Most of these studies leaned towards adopting AI models with a high specificity (over 97%).”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [37]
The Methods > Model inputs table assigns AI screening a specificity of 98.50% in this rural-China economic evaluation. The paper does not establish that most previous health economic studies in China adopted models with specificity over 97%; it provides one example, not a pattern across studies. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrevious health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening.
“Previous health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening, to indicate the cost-effectiveness of using AI for screening DR14,15,37–39.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [37]
The Methods and Results describe a cost-effectiveness comparison of AI screening with ophthalmologist screening, which supports the claim’s core comparison. But they also compare AI screening with no screening, so this study does not support the word “only.” (LLM, conf=0.88)
Experimental — accuracy not yet measured
The utility values for each DR status (non-referable DR, referable DR, and treated referable DR) and blindness were derived from previous studies in China.
“The utility values for each DR status (non-referable DR, referable DR, and treated referable DR) and blindness were derived from previous studies in China that closely matched the situation in the current study14,15,37,39,51,52, and discounted at an annual rate of 3.5% according to the recommendations from the National Institute for Health and Clinical Excellence53.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [37]
The Methods (Model inputs and Effectiveness) report utility values for DR states and blindness drawn from published literature, supporting that part of the claim. But the References identify a principal utility source as a study in India, and the Study limitations explicitly say some utility values came from other countries. The source therefore does not support describing all those values as derived from previous studies in China. (LLM, conf=0.88)
Experimental — accuracy not yet measuredUsing autonomous AI for diabetic retinopathy screening has been shown to be lower cost in other countries.
“using autonomous AI for DR screening has been shown to be lower cost in other countries.13–16”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [14]
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
Global prevalence and major risk factors of diabetic retinopathy
Yau, J. W. Y., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., Chen, S.-J., Dekker, J. M., Fletcher, A., Grauslund, J., et al. (2012) · Diabetes Care · doi:10.2337/dc11-1909 · Cited by 4,985 · see citing works
Flaxman, S. R., Bourne, R. R. A., Resnikoff, S., Ackland, P., Braithwaite, T., Cicinelli, M. V., Das, A., Jonas, J. B., Keeffe, J., Kempen, J. H., et al. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30393-5 · Cited by 3,477 · see citing works
Cheung, N., Mitchell, P., & Wong, T. Y. (2010) · The Lancet · doi:10.1016/s0140-6736(09)62124-3 · Cited by 3,288 · see citing works
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Li, Y., Teng, D., Shi, X., Qin, G., Qin, Y., Quan, H., Shi, B., Sun, H., Ba, J., Chen, B., et al. (2020) · BMJ · doi:10.1136/bmj.m997 · Cited by 1,586 · see citing works
Ting, D. S. W., Cheung, G. C. M., & Wong, T. Y. (2016) · Clinical & Experimental Ophthalmology · doi:10.1111/ceo.12696 · Cited by 1,066 · see citing works
Guidelines on Diabetic Eye Care
Wong, T. Y., Sun, J., Kawasaki, R., Ruamviboonsuk, P., Gupta, N., Lansingh, V. C., Maia, M., Mathenge, W., Moreker, S., Muqit, M. M. K., et al. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.04.007 · Cited by 839 · see citing works
Generalized cost-effectiveness analysis for national-level priority-setting in the health sector
Hutubessy, R. C. W., Chisholm, D., Edejer, T. T.-T., & WHO-CHOICE (2003) · Cost Effectiveness and Resource Allocation · doi:10.1186/1478-7547-1-8 · Cited by 535 · see citing works
Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy
Lin, K., Hsih, W., Lin, Y., Wen, C., & Chang, T. (2021) · Journal of Diabetes Investigation · doi:10.1111/jdi.13480 · Cited by 446 · cited 1× · see citing works
“Considering the importance of regular screening of people with diabetes for timely intervention and reduction of vision impairment [9], it is urgent to take measures to make DR screening more available and affordable in the rural areas of China.”
BackgroundCost-Effectiveness of Detecting and Treating Diabetic Retinopathy
Javitt, J. C., & Aiello, L. P. (1996) · Annals of Internal Medicine · doi:10.7326/0003-4819-124-1_part_2-199601011-00017 · Cited by 411 · see citing works
Song, P., Yu, J., Chan, K. Y., Τheodoratou, E., & Rudan, I. (2018) · Journal of Global Health · doi:10.7189/jogh.08.010803 · Cited by 342 · see citing works
Wong, T. Y., & Sabanayagam, C. (2019) · Ophthalmologica · doi:10.1159/000502387 · Cited by 312 · see citing works
Vijan, S. (2000) · JAMA · doi:10.1001/jama.283.7.889 · Cited by 295 · see citing works
Xie, Y., Nguyen, Q. D., Hamzah, H., Lim, G., Bellemo, V., Gunasekeran, D. V., Yip, M. Y. T., Qi Lee, X., Hsu, W., Li Lee, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30060-1 · Cited by 288 · see citing works
Automated Diabetic Retinopathy Image Assessment Software
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2016.11.014 · Cited by 283 · 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
The Cost–Utility of Telemedicine to Screen for Diabetic Retinopathy in India
Rachapelle, S., Legood, R., Alavi, Y., Lindfield, R., Sharma, T., Kuper, H., & Polack, S. (2012) · Ophthalmology · doi:10.1016/j.ophtha.2012.09.002 · Cited by 171 · see citing works
Feldman-Billard, S., Larger, É., & Massin, P. (2017) · Diabetes & Metabolism · doi:10.1016/j.diabet.2017.10.014 · Cited by 164 · see citing works
Frequency of evidence-based screening for retinopathy in type 1 diabetes
The DCCT/EDIC Research Group (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1612836 · Cited by 141 · see citing works
Scanlon, P. H., Aldington, S. J., Leal, J., Luengo-Fernández, R., Oke, J. L., Sivaprasad, S., Gazis, A., & STRATTON, I. M. (2015) · Health Technology Assessment · doi:10.3310/hta19740 · Cited by 138 · see citing works
Diabetic Retinopathy Screening: A Systematic Review on Patients’ Non-Attendance
Kashim, R., Newton, P., & Ojo, O. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15010157 · Cited by 128 · see citing works
Platania, C. B. M., Maisto, R., Trotta, M. C., D’Amico, M., Rossi, S., Gesualdo, C., D’Amico, G., Baltă, C., Herman, H., Hermenean, A. O., et al. (2019) · British Journal of Pharmacology · doi:10.1111/bph.14665 · Cited by 125 · see citing works
Tang, J., Liang, Y., O’Neill, C., Kee, F., Jiang, J., & Congdon, N. G. (2019) · The Lancet Global Health · doi:10.1016/s2214-109x(19)30201-3 · Cited by 121 · see citing works
Scotland, G., McNamee, P., Philip, S., Fleming, A., Goatman, K. A., Prescott, G. J., Fonseca, S., Sharp, P. F., & Olson, J. A. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2007.120972 · Cited by 108 · see citing works
Review of retinal cameras for global coverage of diabetic retinopathy screening
Rajalakshmi, R., Prathiba, V., Arulmalar, S., & Usha, M. (2020) · Eye · doi:10.1038/s41433-020-01262-7 · Cited by 104 · see citing works
Artificial intelligence-based screening for diabetic retinopathy at community hospital
He, J., Cao, T., Xu, F., Wang, S., Tao, H., Wu, T., Sun, L., & Chen, J. (2019) · Eye · doi:10.1038/s41433-019-0562-4 · Cited by 75 · see citing works
Shah, A., Clarida, W., Amelon, R., Hernaez-Ortega, M. C., Navea, A., Morales-Olivas, J., Dolz-Marco, R., Verbraak, F., Jorda, P. P., van der Heijden, A. A., et al. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820906212 · Cited by 68 · see citing works
Fuller, S. D., Hu, J. Q., Liu, J. C., Gibson, E., Gregory, M. H., Kuo, J., & Rajagopal, R. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820967011 · Cited by 60 · see citing works
Lower visual acuity predicts worse utility values among patients with type 2 diabetes
Smith, D. H., Johnson, E. S., Russell, A., Hazlehurst, B. L., Muraki, C. S., Nichols, G. A., Oglesby, A., & Brown, J. B. (2008) · Quality of Life Research · doi:10.1007/s11136-008-9399-1 · Cited by 45 · see citing works
Assessing the Natural Course of Diabetic Retinopathy: A Population-Based Study in Kinmen, Taiwan
Tung, T., Chen, S., Shih, H., Chou, P., Li, A.-F., Shyong, M., Lee, F.-L., & Liu, J. (2006) · Ophthalmic Epidemiology · doi:10.1080/09286580600826637 · Cited by 40 · see citing works
for the Individualised Screening for Diabetic Retinopathy (ISDR) Study Group, Eleuteri, A., Fisher, A. C., Broadbent, D., García‐Fiñana, M., Cheyne, C. P., Wang, A., STRATTON, I. M., Gabbay, M., Seddon, D., et al. (2017) · Diabetologia · doi:10.1007/s00125-017-4386-0 · Cited by 38 · see citing works
Ming, S., Xie, K., Lei, X., Yang, Y., Zhao, Z., Li, S., Jin, X., & Lei, B. (2021) · International Ophthalmology · doi:10.1007/s10792-020-01685-x · Cited by 36 · see citing works
Zhao, Q., Wang, L., Tao, T., & Xu, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082503 · Cited by 24 · see citing works
Sluiter, R. L., Janzing, J., van der Wilt, G. J., Kievit, W., & Teichert, M. (2019) · The Pharmacogenomics Journal · doi:10.1038/s41397-019-0070-8 · Cited by 19 · see citing works
Progress in examining cost-effectiveness of AI in diabetic retinopathy screening
Dismuke, C. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30077-7 · Cited by 19 · see citing works
Thomas, R., Winfield, T. G., Prettyjohns, M., Dunstan, F., Cheung, W., Anderson, P. M., Peter, R., Luzio, S. D., & Owens, D. R. (2020) · The European Journal of Health Economics · doi:10.1007/s10198-020-01191-y · Cited by 16 · see citing works
Yfantopoulos, J., & Chantzaras, A. E. (2020) · The European Journal of Health Economics · doi:10.1007/s10198-020-01167-y · Cited by 15 · see citing works
Scotland, G., McKeigue, P., Philip, S., Leese, G., Olson, J. A., Looker, H. C., Colhoun, H. M., & Javanbakht, M. (2016) · Diabetic Medicine · doi:10.1111/dme.13129 · Cited by 13 · see citing works
The impact of a co-payment on the cost-effectiveness of screening for diabetic retinopathy
Lian, J., McGhee, S. M., Gangwani, R. A., Chan, C. K. W., Lam, C. L. K., Yap, M. K. H., & Wong, D. (2015) · Journal of Public Health · doi:10.1093/pubmed/fdv168 · Cited by 10 · see citing works
Khalaf, S. S., Bdour, M. D. A., & Al-Till, M. I. (2007) · European Journal of Ophthalmology · doi:10.1177/112067210701700112 · Cited by 9 · see citing works
Li, M., Wang, G., Xia, H., Tang, X., Feng, Z., Yong-yu, Y., Huang, Y., Fan, W., Yuan, Z., & Yuan, J. (2019) · Zhonghua shiyan yanke zazhi · doi:10.3760/cma.j.issn.2095-0160.2019.08.015 · Cited by 6 · see citing works
Self-citationLi, Z. X., Zhang, J., Fong, N., & He, M. (2020) · PubMed · doi:10.3760/cma.j.cn112137-20200901-02526 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [20]Ming S Xie KP Lei X Yang YR Zhao ZX Li SY Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study Int Ophthalmol 2021 41 4 1291 9 33389425
- [23]He J Cao T Xu F Wang S Tao H Wu T Artificial intelligence-based screening for diabetic retinopathy at community hospital Eye (Lond) 2020 34 3 572 576 31455902 PMC7042314
- [26]Zhang WJ Wei M The evaluation of the mortality and life expectancy of chinese population Population Journal 2016 38 03 18 28
- [34]Tang J Liang Y O'Neill C Kee F Jiang J Congdon N Cost-effectiveness and cost-utility of population-based glaucoma screening in China a decision-analytic Markov model Lancet Global Health 2019 7 7 e968 e978 31122906
- [41]Xie Y Nguyen QD Hamzah H Lim G Bellemo V Gunasekeran DV Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study Lancet Digital Health 2020 2 5 E240 E249 33328056
Cited by
Sorted by citations
Zuhair, V., Babar, A., Ali, R., Oduoye, M. O., Noor, Z., Chris, K., Okon, I. I., & Rehman, L. U. (2024) · Journal of Primary Care & Community Health · doi:10.1177/21501319241245847 · Cited by 197 · see citing works
Rajesh, A. E., Davidson, O. Q., Lee, C. S., & Lee, A. (2023) · Diabetes Care · doi:10.2337/dci23-0032 · Cited by 166 · see citing works
Li, Q., & Qin, Y. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04700-8 · Cited by 131 · see citing works
Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare
El Arab, R. A., & Al Moosa, O. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01722-y · Cited by 149 · see citing works
“In diabetic retinopathy screening, both Xie et al. 23 and Huang et al. 36 found that AI-driven models reduced per-patient screening costs by 14 – 19.5% and achieved ICERs as low as $1107.63 per QALY.”
InfluentialOculomics: Current concepts and evidence
Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101350 · Cited by 94 · see citing works
Artificial Intelligence in Medicine and Dentistry
Vodanović, M., Subašić, M., Milošević, D., & Pavičin, I. S. (2023) · Acta Stomatologica Croatica · doi:10.15644/asc57/1/8 · Cited by 88 · see citing works
Elvidge, J., Hawksworth, C., Avşar, T. S., Zemplenyi, A. T., Chalkidou, A., Petrou, S., Petykó, Z. I., Srivastava, D., Chandra, G., Delaye, J., et al. (2024) · Value in Health · doi:10.1016/j.jval.2024.05.006 · Cited by 80 · see citing works
Wang, Y., Liu, C., Hu, W., Luo, L., Shi, D., Zhang, J., Yin, Q., Zhang, L., Han, X., & He, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01032-9 · Cited by 76 · see citing works
Vithlani, J., Hawksworth, C., Elvidge, J., Ayiku, L., & Dawoud, D. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1220950 · Cited by 65 · see citing works
Zhang, Z., Deng, C., & Paulus, Y. M. (2024) · Biomedicines · doi:10.3390/biomedicines12071405 · Cited by 65 · see citing works
Cleland, C. R., Rwiza, J., Evans, J. R., Gordon, I., Macleod, D., Burton, M. J., & Bascarán, C. (2023) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2023-003424 · Cited by 64 · see citing works
Artificial intelligence for retinal diseases
Lim, J. I., Rachitskaya, A. V., Hallak, J., Gholami, S., & Alam, M. N. (2024) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2024.100096 · Cited by 53 · see citing works
Artificial intelligence for telemedicine diabetic retinopathy screening: a review
Nakayama, L. F., Ribeiro, L. Z., Novaes, F. D. C., Miyawaki, I. A., Miyawaki, A. E., de Oliveira, J. A. E., de Oliveira, T. V. F., Malerbi, F. K., Regatieri, C. V., Celi, L. A., et al. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2258149 · Cited by 49 · see citing works
Hu, W., Joseph, S., Li, R., Woods, E., Sun, J., Shen, M., Jan, C. L., Zhu, Z., He, M., & Zhang, L. (2024) · eClinicalMedicine · doi:10.1016/j.eclinm.2023.102387 · Cited by 47 · see citing works
Wu, H., Jin, K., Yip, C. C., Koh, V., & Ye, J. (2024) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2024.03.008 · Cited by 41 · see citing works
Srisubat, A., Kittrongsiri, K., Sangroongruangsri, S., Khemvaranan, C., Shreibati, J. B., Ching, J., Hernandez, J., Tiwari, R., Hersch, F. B., Liu, Y., et al. (2023) · Ophthalmology and Therapy · doi:10.1007/s40123-023-00688-y · Cited by 42 · see citing works
Gu, C., Wang, Y., Jiang, Y., Xu, F., Wang, S., Liu, R., Yuan, W., Abudureyimu, N., Wang, Y., Lu, Y., et al. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-322940 · Cited by 39 · see citing works
Self-citationArtificial intelligence for early detection of diabetes mellitus complications via retinal imaging
Sobhi, N., Sadeghi-Bazargani, Y., Mirzaei, M., Abdollahi, M., Jafarizadeh, A., Pedrammehr, S., Alizadehsani, R., Tan, R.-S., Islam, S. M. S., & Acharya, U. R. (2025) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-025-01596-7 · Cited by 39 · see citing works
Artificial intelligence for access to primary healthcare in rural settings
Lamem, M. F. H., Sahid, M. I., & Ahmed, A. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100173 · Cited by 46 · see citing works
Kastrup, N., Holst‐Kristensen, A. W., & Valentin, J. B. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00088-7 · Cited by 34 · see citing works
Ferrier, C., Bendifallah, S., Suisse, S., Dabi, Y., Touboul, C., Puchar, A., Zarca, K., & Durand‐Zaleski, I. (2022) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.17348 · Cited by 33 · see citing works
Wu, X., Wu, Y., Tu, Z., Cao, Z., Xu, M., Xiang, Y., Lin, D., Jin, L., Zhao, L., Zhang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47211-w · Cited by 28 · see citing works
Li, X., Zong, Q., & Cheng, M. (2024) · Journal of Nursing Management · doi:10.1155/2024/8885760 · Cited by 31 · see citing works
Self-citationCurrent and future roles of artificial intelligence in retinopathy of prematurity
Jafarizadeh, A., Maleki, S. F., Pouya, P., Sobhi, N., Abdollahi, M., Pedrammehr, S., Lim, C. P., Asadi, H., Alizadehsani, R., Tan, R., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11153-6 · Cited by 29 · see citing works
Alqahtani, A. S., Alshareef, W. M., Aljadani, H. T., Hawsawi, W. O., & Shaheen, M. H. (2025) · International Journal of Retina and Vitreous · doi:10.1186/s40942-025-00670-9 · Cited by 37 · see citing works
Li, H., Li, G., Li, N., Liu, C., Yuan, Z., Gao, Q., Hao, S., Fan, S., & Yang, J. (2023) · PLOS ONE · doi:10.1371/journal.pone.0291390 · Cited by 27 · see citing works
Sarayar, R., Lestari, Y. D., Setio, A. A. A., & Sitompul, R. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1239231 · Cited by 26 · see citing works
Yu, T., Shao, A., Wu, H., Su, Z., Shen, W., Zhou, J., Lin, X., Shi, D., Grzybowski, A., Wu, J., et al. (2025) · Ophthalmology and Therapy · doi:10.1007/s40123-025-01109-y · Cited by 23 · see citing works
The role of saliency maps in enhancing ophthalmologists’ trust in artificial intelligence models
Wong, C. Y. T., Antaki, F., Woodward-Court, P., Ong, A. Y., & Keane, P. A. (2024) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2024.100087 · Cited by 20 · see citing works
Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective
Ahmed, M., Dai, T., Channa, R., Abràmoff, M. D., Lehmann, H. P., & Wolf, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01382-4 · Cited by 20 · see citing works
White, T., Selvarajah, V., Sand, F. W., Svangård, N., Mohankumar, G., Fenici, P., Rough, K., Onyango, N. O., Lyons, K., Mack, C. D., et al. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15587 · Cited by 19 · see citing works
Li, Z., Yin, S., Wang, S., Wang, Y., Qiang, W., & Jiang, J. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.11.018 · Cited by 14 · see citing works
Rizvi, A., Rizvi, F., Lalakia, P., Hyman, L., Frasso, R., Sztandera, L., & Das, A. V. (2023) · Cureus · doi:10.7759/cureus.45539 · Cited by 16 · see citing works
Yang, Y., Tan, J., He, Y., Huang, H., Wang, T., Gong, J., Liu, Y., Zhang, Q., & Xu, X. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1099302 · Cited by 14 · see citing works
Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment
Zeppieri, M., Gagliano, C., Tognetto, D., Musa, M., Avitabile, A., D’Esposito, F., Nicolosi, S. G., & Capobianco, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14186519 · Cited by 11 · see citing works
Challenges and Potential of Artificial Intelligence in Neuroradiology
Winder, A. J., Stanley, E. A. M., Fiehler, J., & Forkert, N. D. (2024) · Clinical Neuroradiology · doi:10.1007/s00062-024-01382-7 · Cited by 11 · see citing works
Evaluation of ChatGPT-4 in detecting referable diabetic retinopathy using single fundus images
Aftab, O., Khan, H., VanderBeek, B. L., Scoles, D., Kim, B. J., & Tsui, J. C. (2025) · AJO International · doi:10.1016/j.ajoint.2025.100111 · Cited by 10 · see citing works
Comparing code-free and bespoke deep learning approaches in ophthalmology
Wong, C. Y. T., O’Byrne, C., Taribagil, P., Liu, T., Antaki, F., & Keane, P. A. (2024) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-024-06432-x · Cited by 10 · see citing works
Chen, Y., Song, F., Zhao, Z., Wang, Y., To, E., Liu, Y., Shi, D., Chen, X., Xu, L., Shang, X., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112161 · Cited by 10 · see citing works
Burmeister, J., Pham, M. N., Bohler, F., & North, C. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2522323 · Cited by 9 · see citing works
Deep learning detection of diabetic retinopathy in Scotland’s diabetic eye screening programme
Fleming, A. D., Mellor, J., McGurnaghan, S., Blackbourn, L., Goatman, K., Styles, C., Storkey, A., McKeigue, P., & Colhoun, H. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-323395 · Cited by 8 · see citing works
Leigh, J., Drinkwater, J., Turner, A., & Schroeder, E. (2026) · Clinical & Experimental Ophthalmology · doi:10.1111/ceo.70016 · Cited by 8 · see citing works
Ran, A. R., Ding, J. L., Tang, Z., Lam, C., Nguyen, T. X., Zhou, J., Zhang, S., Fang, D., Yang, D., Ng, V., et al. (2026) · Diabetes Care · doi:10.2337/dc25-1493 · Cited by 7 · see citing works
Artificial Intelligence in Nursing Education: Challenges and Opportunities in the Chinese Context
Jiang, Y. (2024) · Open Journal of Social Sciences · doi:10.4236/jss.2024.1211004 · Cited by 9 · see citing works
Lin, S., Ma, Y., Li, L., Jiang, Y., Peng, Y., Yu, T., Qian, D., Xu, Y., Lu, L., Chen, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109329 · Cited by 6 · see citing works
Recent evidence of economic evaluation of artificial intelligence in ophthalmology
Ruamviboonsuk, P., Ruamviboonsuk, V., & Tiwari, R. (2023) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000987 · Cited by 5 · see citing works
Screening of Diabetic Retinopathy Using Artificial Intelligence and Tele-Ophthalmology
Kuklinski, E. J., Henry, R. K., Shah, M., Zarbin, M. A., Szirth, B. C., & Bhagat, N. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231194041 · Cited by 5 · see citing works
Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective
Grzybowski, A. E., Liu, T. Y. A., Popovic, M. M., Zhao, A., Miyake, M., Takahashi, H., Kawasaki, R., He, M., Gong, X., Göktaş, P., et al. (2026) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2026.100328 · Cited by 4 · see citing works
Lam, R. F., Radke, N. V., Bandello, F. M., Bhende, P., Chen, Y., Cheung, C. M. G., Chhablani, J., Fung, A. T.-C., Hsieh, Y., Lai, C., et al. (2025) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2025.100262 · Cited by 4 · see citing works
Self-citationZhou, J., Hu, C., Chen, J., Liu, J., & Li, X. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70621 · Cited by 4 · see citing works
Self-citationÇALIŞKAN, G., & Çınaroğlu, S. (2023) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1197021 · Cited by 4 · see citing works
Ramoutar, R. R. (2024) · Cureus · doi:10.7759/cureus.55745 · Cited by 4 · see citing works
González, R. A., Susanna-González, G., Blair, J., Vitar, R. M. L., Ciller, C., Apostolopoulos, S., Zanet, S. I. D., Martin, J., Bermúdez, C., Pascual, A. L. C., et al. (2025) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2024-002109 · Cited by 3 · see citing works
Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review
Injante, R., & Julca, M. (2024) · LatIA · doi:10.62486/latia2024112 · Cited by 3 · see citing works
Artificial Intelligence for Early Detection and Prognosis Prediction of Diabetic RetinopathyPreprint
Susilo, Y. K. B., Yuliana, D., Mahadi, M., Rahman, S. A., & Ariffin, A. E. (2025) · medRxiv · doi:10.1101/2025.03.29.25324873 · Cited by 3 · see citing works
Lee, J. T., Shen, T. K.-B., Liu, V. T.-N., Chen, T. H.-H., Lee, C., Lu, C., Huang, C., & Atun, R. A. (2025) · medRxiv · doi:10.1101/2025.10.27.25338867 · Cited by 3 · see citing works
Lin, S., Zhang, J., Jan, C., Li, L., Peng, Y., Qian, D., Yin, Y., Zhang, M., Feng, J., He, M., et al. (2025) · British Journal of Ophthalmology · doi:10.1136/bjo-2024-325174 · Cited by 2 · see citing works
Vidal‐Alaball, J., Bonache, A. A., Solé‐Casals, J., Fibla, D. R., Marin-Gomez, F. X., Distéfano, L., Boixadera, A., Casado-García, Á., García-Domínguez, M., Inés, A., et al. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1754682 · Cited by 9 · see citing works
Liu, T. Y. A., & Wolf, R. M. (2025) · Diabetes Spectrum · doi:10.2337/dsi24-0016 · Cited by 2 · see citing works
su, Z., Li, H., Zheng, Y., Xie, P., Ng, T. K., Qiu, K., & Zhang, G. (2023) · Research Square · doi:10.21203/rs.3.rs-3584831/v1 · Cited by 2 · see citing works
AI Diabetic Retinopathy Screening in a Primary Care Setting in Rural Maine
Heuer, R., DayBranch, E., Tsomides, A., DiBiase, J. F., Korsen, N., & Nolan, B. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-024-09319-z · Cited by 1 · see citing works
Cost-effectiveness analysis of human–AI collaboration in disease screening
Wang, Y., Han, X., Hu, W., Zhou, K., Liu, C., Zhang, J., Zhu, Z., Joseph, S., Yin, Q., Luo, L., et al. (2026) · Nature Health · doi:10.1038/s44360-026-00142-w · Cited by 2 · see citing works
Cost-effectiveness of Artificial Intelligence applications in healthcare: A systematic review
Imani, A., & Monaghesh, E. (2026) · Health Informatics Journal · doi:10.1177/14604582261460314 · Cited by 1 · see citing works
Dylan, H., Lim, G. E., Liliko, M. W., & Gunawan, A. (2024) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss62896.2024.10751457 · Cited by 1 · see citing works
Hosseinkhani, N. T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385517 · Cited by 2 · see citing works
KANKARIA, A., Duggal, M., Kernohan, A., Chauhan, A., Verma, P., Bryant, A., Miglani, V., Chadda, N., Syal, P., Jaiswal, N., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3149178/v1 · Cited by 1 · see citing works
Di Matteo, L., Whitestone, N., & Bhambhwani, V. (2026) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2026-002904
Revolutionizing Diabetes Care: The Pivotal Role of Artificial Intelligence (Preprint)Preprint
Dey, A. K., & Grover, A. (2023) · doi:10.2196/preprints.54056
Estudio bibliométrico y análisis de redes de citación de los screening visuales en atención primaria
Coca-Serrano, R., Sánchez‐Tena, M. Á., Alvarez-Peregrina, C. C., Martínez-Pérez, C., & Moriche-Carretero, M. (2024) · Medicina de Familia SEMERGEN · doi:10.1016/j.semerg.2024.102225
Introduction to Artificial Intelligence in Diabetes
Zale, A. D., Abusamaan, M. S., & Mathioudakis, N. N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13244-5.00019-5
Online Artificial Intelligence Chatbot Aptitude in Detection of Referable Diabetic RetinopathyPreprint
Tsui, J. C., Aftab, O. M., Khan, H. N., VanderBeek, B. L., Scoles, D., & Kim, B. B. (2024) · Research Square · doi:10.21203/rs.3.rs-4530651/v1
Artificial Intelligence and Optometry: Transforming Practice and Patient Care
Majithia, S., & Thakur, S. (2024) · Current practices in ophthalmology · doi:10.1007/978-981-97-8140-9_11
Artificial intelligence could boost eye care in low-income countries
Worth, T. (2025) · Nature · doi:10.1038/d41586-025-00658-3
Artificial Intelligence in Diabetic Retinopathy
Grzybowski, A. E., & Brona, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-83756-2_11 · Cited by 1 · see citing works
Mohd, S., Kaur, R., & Singh, T. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-314-420251026
Farah, L. (2024) · theses.fr (ABES) · doi:10.70675/2826eff9z5f56z4a53za618z7d4afbe7e069
Wei, C., Zhang, L., Chen, C., Le, Y., Feng, Q., Li, Z., & Leng, Z. (2026) · Value in Health · doi:10.1016/j.jval.2026.06.005
Autonomous AI for Diabetic Retinopathy Screening: Evidence, Regulation, and Health-System Fit
Mehta, K., Patel, L., & Shah, M. (2026) · Clinical eHealth · doi:10.1016/j.ceh.2026.07.001
Gupta, E., Aung, M. H., & Bowden, E. C. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15155827
Hu, Y., Wang, X., Ji, Y., Chen, Y., Liang, Z., Wu, S., Lai, M., & Yang, W. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1909816
Applications of artificial intelligence in rural ophthalmology: a scoping review
Armstrong, T. S., Mohamed, A., Srivastava, O., & TENNANT, M. T. S. (2026) · Canadian Journal of Ophthalmology · doi:10.1016/j.jcjo.2026.08.005
Grønlykke, L., Wiberg, S. C., Carter, A., Ratgen, P. H., Lowater, S. J., Andersen, J., Savarimuthu, T. R., & Grauslund, J. (2026) · Acta Ophthalmologica · doi:10.1111/aos.70225
Hooshmand-Moghadam, B., Talebpour, M., & Keshtidar, M. (2026) · Journal of Translational Medical Research · doi:10.66224/jbums.33.60
Chen, W., zhao, D., Wu, L., Bai, W., Wang, X., Zhang, Y. Y., Niu, J.-Y., & Yu, C. (2026) · BMC Nephrology · doi:10.1186/s12882-026-05353-7
Liu, Y.-X., & Liu, H.-R. (2026) · Eye & ENT Research · doi:10.1002/eer3.70059
Başer, O. (2026) · Journal of health economics and outcomes research · doi:10.36469/001c.171942
Başer, O. (2026) · Journal of health economics and outcomes research · doi:10.36469/jheor.2026.171942
- Authors
- Huang, X.-M., Yang, B.-F., Zheng, W.-L., Liu, Q., Xiao, F., Ouyang, P.-W., Li, M.-J., Li, X.-Y., Meng, J., Zhang, T.-T., Cui, Y.-H., & Pan, H.-W.
- Affiliations
- Jinan University (CN)
- Venue
- BMC Health Services Research
- Published
- 2022-02-25
- Volume
- 22
- Issue
- 1
- Pages
- 260-260
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC8881835
- OpenAlex
- W4214726824
- Cited by
- 89
- References
- 42
- Citations by year
- 2026: 17 · 2025: 24 · 2024: 26 · 2023: 20 · 2022: 1
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National College Students Innovation and Entrepreneurship Training Program (S202010559065)
- Guangdong Medical Research Fund (A2019158)
- 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 screening, cost-effectiveness, rural China, fundus images, quality-adjusted life years, incremental cost-effectiveness ratio, Markov model, hybrid decision tree, blindness prevention, ophthalmologist shortage
- MeSH terms
- Artificial Intelligence, China, Cost-Benefit Analysis, Diabetes Mellitus, Diabetic Retinopathy, Humans, Mass Screening, Quality-Adjusted Life Years
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
