What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI ‐assisted screening for diabetic eye disease?
Year: 2025
Type: article
Study design: Observational Study
Abstract: AIMS: To explore attitudes of people living with diabetes (PLD) and healthcare professionals (HCP) towards the use of automated retinal image analysis systems using artificial intelligence (AI) in NHS Diabetic Eye Screening Programmes (DESP) and how these perceptions vary by sociodemographic subgroups. METHODS: Two anonymous online surveys (28 questions for PLD and 21 for HCP) were developed to assess attitudes towards AI. Data were collected from four English DESPs, diabetes charities and patient groups between September and December 2023. Likert-scale responses were analysed using regression to examine subgroup differences. RESULTS: A total of 1577 PLD and 262 HCP participated. Fifty-eight per cent of PLD believed AI would perform equally well in all subgroups, compared with 32% of HCP. Seventy-one per cent of HCP disagreed that AI could replace human grading, and 81% of PLD felt humans should remain responsible for screening outcomes. Both groups supported AI's efficiency but had concerns about data security, trust, job security and who would be responsible for AI errors. Linear regression of Likert scores showed women were less accepting of AI; PLD of Black and Asian ethnicities were more cautious of data security and impact on screening experience. HCP of Asian ethnicity generally held more negative views across themes. Those using more online applications had more positive views towards AI. CONCLUSIONS: While both PLD and HCP recognise AI's potential benefits, concerns regarding security, job impact and errors highlight the need for targeted outreach based on sociodemographic factors.
Source: Diabetic Medicine
DOI: 10.1111/dme.70165
Authors: Kathryn Willis, Royce Shakespeare, Lakshmi Chandrasekaran, Umar Chaudhry, Charlotte Wahlich, Ryan Chambers, Louis Bolter, John Anderson, Abraham Olvera‐Barrios, Jiri Fajtl, Roshan Welikala, Sarah Barman, Samantha Mann, Peter Scanlon, Maged S. Habib, Catherine A. Egan, Adnan Tufail, Christopher G. Owen, Alicja R. Rudnicka
Institutions: University of London, St George's, University of London, City St George's, University of London, Homerton University Hospital NHS Foundation Trust, Moorfields Eye Hospital NHS Foundation Trust, NIHR Moorfields Biomedical Research Centre, University College London, Kingston University London, Guy's and St Thomas' NHS Foundation Trust, Gloucestershire Hospitals NHS Foundation Trust, South Tyneside and Sunderland NHS Foundation Trust
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,870
OpenAlex ID: W4416079397
