Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data
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Abstract
Background: Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)–based and manual grading–based telemedicine screening is inadequate for policy making. Objective: The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. Methods: We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita. Results: The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy. Conclusions: Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.
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Previous studies only focused on theoretical scenarios where sensitivity and specificity can change independently.
“However, these studies only focused on theoretical scenarios where sensitivity and specificity can change independently, overlooking that in practical screening tools, these two parameters have an inverse correlation.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [15]
The Discussion’s Principal Findings (page unavailable) says this study modeled practical screening approaches using real-world data from an ongoing program in urban China. That contradicts the characterization that it focused only on theoretical scenarios. The Methods’ Sensitivity Analysis describes varying sensitivity and specificity, but does not say they can change independently or discuss an inverse correlation between them. (LLM, conf=0.87)
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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 [15]
Methods > Screening Accuracy (page unavailable) says this Shanghai economic evaluation used an AI model with 97.96% specificity for sight-threatening diabetic retinopathy. The paper does not say that most previous health economic studies in China adopted models with specificity above 97%, so the claim extends a finding about this study to prior studies generally. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe 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 [15]
The Methods section, “Utility and QALYs” (page unavailable), says the utility values were based on published studies from China and other Asian countries and gives values for non-STDR, STDR, and blindness. It does not give a separate utility value for treated referable DR, and it does not say all the underlying studies were from China. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPrevious studies evaluating cost-effectiveness of AI models assigned different values to sensitivity and specificity.
“To evaluate cost-effectiveness change, previous studies assigned different values to the sensitivity and specificity of AI models14–16.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [15]
Previous 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 [15]
In urban China, an AI-assisted community telemedicine model was not automatically cost-effective compared with manual grading, but its cost-effectiveness improved when referral compliance increased.
“In urban China, Lin and colleagues found that an AI-assisted community telemedicine model was not automatically cost-effective compared with manual grading, but its value improved when referral compliance increased (24).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [24]
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 [15]
The Methods sections “Model Overview” and “Prevalence and Transition Probabilities” describe annual progression between diabetic-retinopathy stages and the sources of transition probabilities, but give no 7% rate for progression from non-referable to referable DR. Searches for “7%,” “0.07,” and the named transition found no such estimate in our copy; the paper points to multimedia appendices for detailed parameters. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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- Authors
- Lin, S., Ma, Y., Xu, Y., Lu, L., He, J., Zhu, J., Peng, Y., Yu, T., Congdon, N., & Zou, H.
- Affiliations
- Shanghai Eye Disease Prevention & Treatment Center (CN)
- Queen's University Belfast (GB)
- Corresponding author
- Zou, Haidong
- ORCID
- Venue
- JMIR Public Health and Surveillance
- Publisher
- JMIR Publications Inc.
- Published
- 2023-02-23
- Volume
- 9
- Pages
- e41624
- ISSN
- 2369-2960 (ISSN-L)
- PMC
- PMC9999255
- OpenAlex
- W4321612624
- Cited by
- 52
- References
- 40
- Citations by year
- 2026: 17 · 2025: 16 · 2024: 14 · 2023: 5
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Public Health and Surveillance (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Shanghai Municipal Health Commission (2022YQ051)
- Shanghai Hospital Development Center
- National Key Research and Development Program of China (19DZ2250100, 2021YFC2702100)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy, telemedicine screening, artificial intelligence, manual grading, cost-effectiveness, cost-utility analysis, community-based screening, low- and middle-income countries, referral compliance, quality-adjusted life years, urban China, labor costs, blindness prevention, Markov model, real-world data, sensitivity analysis, incremental cost-effectiveness
- MeSH terms
- Aged, Artificial Intelligence, Blindness, China, Cost-Benefit Analysis, Diabetes Mellitus, Diabetic Retinopathy, Humans, Markov Chains, Telemedicine
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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.
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