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Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme.

Wahlich, C; Chandrasekaran, L; Chaudhry, UAR; Willis, K; Chambers, R; Bolter, L; Anderson, J; Shakespeare, R; Olvera-Barrios, A; Fajtl, J; et al. Wahlich, C; Chandrasekaran, L; Chaudhry, UAR; Willis, K; Chambers, R; Bolter, L; Anderson, J; Shakespeare, R; Olvera-Barrios, A; Fajtl, J; Welikala, R; Barman, S; Egan, CA; Tufail, A; Owen, CG; Rudnicka, AR (2024) Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme. Diabetes Res Clin Pract, 219. p. 111964. ISSN 1872-8227 https://doi.org/10.1016/j.diabres.2024.111964
SGUL Authors: Wahlich, Charlotte Amy

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Abstract

AIMS: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in the NHS Diabetic Eye Screening Programme (DESP) in England. This study aims to assess perceptions of People Living with Diabetes (PLD) and Healthcare Practitioners (HCP) towards AI's introduction in DESP. METHODS: Two online surveys were co-developed with PLD and HCP from a diverse DESP in North East London. Surveys were validated through interviews across three centres and distributed via DESP centres, charities, and the British Association of Retinal Screeners. A coding framework was used to analyse free-text responses. RESULTS: 387 (24%) PLD and 98 (37%) HCP provided comments. Themes included trust, workforce impact, the patient-practitioner relationship, AI implementation challenges, and inequalities. Both groups agreed AI in DESP was inevitable, would improve efficiency, and save costs. Concerns included job losses, data security, and AI decision safety. A common misconception was that AI would directly affect patient interactions, though it only processes retinal images. CONCLUSIONS: Limited understanding of AI was a barrier to acceptance. Educating diverse PLD groups and HCP about AI's accuracy and reliability is crucial to building trust and facilitating its integration into screening practices.

Item Type: Article
Additional Information: Crown Copyright © 2024 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Artificial intelligence, Diabetes, Qualitative, Screening, Technology, 1103 Clinical Sciences, 1117 Public Health and Health Services, 1701 Psychology, Endocrinology & Metabolism
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: Diabetes Res Clin Pract
ISSN: 1872-8227
Language: eng
Dates:
DateEvent
26 December 2024Published
19 December 2024Published Online
13 December 2024Accepted
Publisher License: Creative Commons: Attribution 4.0
PubMed ID: 39709112
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/117053
Publisher's version: https://doi.org/10.1016/j.diabres.2024.111964

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