Macdonald, T;
Zhelev, Z;
Liu, X;
Hyde, C;
Fajtl, J;
Egan, C;
Tufail, A;
Rudnicka, AR;
Shinkins, B;
Given-Wilson, R;
et al.
Macdonald, T; Zhelev, Z; Liu, X; Hyde, C; Fajtl, J; Egan, C; Tufail, A; Rudnicka, AR; Shinkins, B; Given-Wilson, R; Dunbar, JK; Halligan, S; Scanlon, P; Mackie, A; Taylor-Philips, S; Denniston, AK
(2025)
Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.
The Lancet Digital Health, 7 (5).
p. 100840.
ISSN 2589-7500
https://doi.org/10.1016/j.landig.2024.12.004
SGUL Authors: Rudnicka, Alicja Regina
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Abstract
Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial intelligence, commonly known as automated retinal imaging analysis software (ARIAS). A number of such products are now on the market. In the UK, Scotland has used a rules-based autograder since 2011, but the diabetic eye screening programmes in the rest of the UK rely solely on human graders. With more sophisticated machine learning-based ARIAS now available and greater challenges in terms of human grader capacity, in 2019 the UK's National Screening Committee (NSC) was asked to consider the modification of diabetic eye screening in England with ARIAS. Following up on a review of ARIAS research highlighting the strengths and limitations of existing evidence, the NSC here sets out their considerations for evaluating evidence to support the introduction of ARIAS into the diabetic eye screening programme.
Item Type: | Article |
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Additional Information: | Copyright © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. |
Keywords: | Humans, Diabetic Retinopathy, Mass Screening, Artificial Intelligence, United Kingdom |
SGUL Research Institute / Research Centre: | Academic Structure > Population Health Research Institute (INPH) |
Journal or Publication Title: | The Lancet Digital Health |
ISSN: | 2589-7500 |
Language: | en |
Media of Output: | Print-Electronic |
Related URLs: | |
Publisher License: | Creative Commons: Attribution 4.0 |
PubMed ID: | 40185647 |
Go to PubMed abstract | |
URI: | https://openaccess.sgul.ac.uk/id/eprint/117783 |
Publisher's version: | https://doi.org/10.1016/j.landig.2024.12.004 |
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