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Lookup NU author(s): Dr Jeffry Hogg, Professor Kevin WilsonORCiD, Professor Anya Hurlbert, Professor Jenny ReadORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.
Author(s): Nichol B, Hogg HDJ, Pepper GV, Hamoonga VM, Wilson KJ, Hurlbert AC, Read JCA
Publication type: Article
Publication status: Published
Journal: Royal Society Open Science
Year: 2022
Volume: 11
Issue: 4
Print publication date: 01/12/2022
Online publication date: 01/10/2022
Acceptance date: 02/09/2022
Date deposited: 16/04/2025
ISSN (electronic): 2054-5703
Publisher: The Royal Society Publishing
URL: https://doi.org/10.1177/22799036221127627
DOI: 10.1177/22799036221127627
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