ArticleNPJ digital medicine2024
Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Economic evaluation of AI-assisted technologies in healthcare: A systematic review.Journal of food and drug analysis · 2025Pooled it
- Cost-utility analysis of deep learning-based opportunistic osteoporosis screening using chest radiographs: implications of diagnostic thresholds and confirmatory testing.Archives of osteoporosis · 2026Article
- Closing Editorial: Toward Safe, Equitable, and Intelligent Healthcare.Healthcare (Basel, Switzerland) · 2026Article
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Predicting Vision-Threatening Retinopathy in Patients With Diabetes Using Patient-Reported Data: Impact on Reading Center Workflow Efficiency.Translational vision science & technology · 2026Article
- Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis.BMJ open ophthalmology · 2026Article
- AI framework for multidisease detection via retinal imaging.Nature medicine · 2026Article
- Acceptable accuracy for medical AI: a survey of physicians and the general population in Sweden.BMJ health & care informatics · 2026Article
- Article
- Characterizing patients who benefit from mature medical AI models in real-world clinical applications.PLOS digital health · 2026Article
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- AI-Assisted Detection of Macular OCT Abnormalities by Optometrists: A Retrospective Reader Study.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening.Frontiers in public health · 2026Review
- Imaging pathways in spondyloarthritis: integrating radiography, ultrasonography, magnetic resonance imaging, low-dose computed tomography, and artificial intelligence methods : Radiology and AI in SpA.Rheumatology international · 2025Review
- Cost-Benefit Analysis of Preventing Acute Care Use in Oncology Patients Following Systemic Therapy Using Medicare Claims Data: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
- AI-Automated Risk Operative Stratification for Severe Aortic Stenosis: A Proof-of-Concept Study.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Drug-Coated Cardiovascular Devices: A Narrative Review.Reviews in cardiovascular medicine · 2025Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
Abstract
Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.