Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program
Author:
Publisher
Springer Science and Business Media LLC
Subject
Ophthalmology
Link
https://link.springer.com/content/pdf/10.1007/s40123-023-00688-y.pdf
Reference40 articles.
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3. Zhang Y, Shi J, Peng Y, Zhao Z, Zheng Q, Wang Z, et al. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care. 2020;8:e001596.
4. Ruamviboonsuk P, Chantra S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic evaluations of artificial intelligence in ophthalmology. Asia-Pacific J Ophthalmol. 2021;10:307–16.
5. Beede E, Baylor E, Hersch F, Iurchenko A, Wilcox L, Ruamviboonsuk P, et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In: Proc 2020 CHI conf hum factors comput syst. New York: ACM; 2020. p. 1–12
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1. Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness;Diabetes Care;2023-09-20
2. Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries?;Cureus;2023-09-19
3. Recent evidence of economic evaluation of artificial intelligence in ophthalmology;Current Opinion in Ophthalmology;2023-07-17
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