Evidence map›Paper›PMID 41212985›Full record

ArticleDiabetic medicine : a journal of the British Diabetic Association2026

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?

Kathryn Willis, Royce Shakespeare, Lakshmi Chandrasekaran, Umar Chaudhry, Charlotte Wahlich, Ryan Chambers, Louis Bolter, John Anderson, Abraham Olvera-Barrios, Jiri Fajtl and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetic medicine : a journal of the British Diabetic Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Cohort Profile: The North East London Diabetes Study (NELDS).International journal of epidemiology · 2026
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Kathryn WillisSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Royce ShakespeareSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Lakshmi ChandrasekaranSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Umar ChaudhrySchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Charlotte WahlichSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Ryan ChambersHomerton Healthcare NHS Foundation Trust, London, UK.
Louis BolterHomerton Healthcare NHS Foundation Trust, London, UK.
John AndersonHomerton Healthcare NHS Foundation Trust, London, UK.
Abraham Olvera-BarriosNIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK.
Jiri FajtlSchool of Computer Science and Mathematics, Kingston University, London, UK.
Roshan WelikalaSchool of Computer Science and Mathematics, Kingston University, London, UK.
Sarah BarmanSchool of Computer Science and Mathematics, Kingston University, London, UK.
Samantha MannGuy's and St Thomas's NHS Foundation Trust, London, UK.
Peter ScanlonGloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK.ORCID https://orcid.org/0000-0001-8513-710X
Maged S HabibSouth Tyneside and Sunderland NHS Foundation Trust, South Shields, UK.
Catherine A EganNIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK.
Adnan TufailNIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK.
Christopher G OwenSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
Alicja R RudnickaSchool of Health and Medical Sciences, Department of Population Health and Policy, City St George's, University of London, London, UK.
ARIAS Research Group

Funding

National Institute for Health and Care ResearchNHS Transformation DirectorateSt George's, University of London Participatory FundThe Health FoundationWellcome TrustWellcome Trust (Collaborative Award in Science)
6 · The paper itself

Abstract

aimsTo 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.

methodsTwo 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.

resultsA 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.

conclusionsWhile 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.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelDiabetes MellitusDiabetic RetinopathyHealth PersonnelMass ScreeningAdultAgedFemaleHumansMaleMiddle AgedState MedicineSurveys and QuestionnairesUnited Kingdomartificial intelligencediabetesscreeningsurveytechnology

Identifiers

PMID41212985
PMCPMC12700512

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.