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. 2024 Feb 21;7:43. doi: 10.1038/s41746-024-01032-9

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — AI artificial intelligence, DR diabetic retinopathy, FP false positive, TN true negative, TP true positive, FN false negative. The cost-effectiveness analysis was based on a nationwide DR screening program (the Lifeline Express Program) comprising 251,535 individuals with diabetes. In this cohort, participants accepted annual DR screening using a previously validated AI. Participants classified as referable DR or ungradable were suggested referral to ophthalmologists, and those with referable DR diagnosed by ophthalmologists would be suggested for appropriate treatment. Participants can choose to accept or refuse these suggestions. Participants with no DR, non-referable DR and untreated referable DR will follow the natural DR progression process without treatment, while those who accepted treatment were estimated to continue treatment in subsequent years without entering the following cycles of DR screening. Those who refused DR treatment, and those already blind would also not enter DR screening in the subsequent years. Individuals were estimated to have a risk of death in any state.