Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Concerns about data security are a barrier to AI adoption in diabetes management.
“Another fundamental barrier to consider is the concern about data security, liability, and how the system made the decision (black box problem) [54,55,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Section 3.1.4 (page unavailable) reports data-security concerns and questions about who is responsible if AI misses disease; section 3.1.1 describes limited understanding of AI as a barrier to acceptance. These findings concern AI in diabetic eye screening, not diabetes management generally, and the copy does not specifically identify difficulty explaining an individual AI decision as a barrier. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTime-saving is a facilitator to AI adoption in diabetes management.
“the time sparing effect, both for the patient and for the physician [53,56,57]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Section 3.1.8 (Efficiency; page unavailable) reports that patients and healthcare professionals expected AI to speed up diabetic eye screening and free staff for other tasks. Section 3.1.7 (Practicalities of AI implementation; page unavailable) also records a patient's expectation of faster results. These are perceived benefits in eye screening, not demonstrated time savings for both patients and physicians across diabetes management. (LLM, conf=0.82)
Experimental — accuracy not yet measuredImpact of a grading workload is a barrier in the implementation process domain.
“and the impact of a grading workload [57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Section 3.1.3 (Impact on the workforce; page unavailable) reports that practitioners expected AI to reduce graders’ workload but worried this could lead to job losses or cuts rather than reinvestment. This supports concern about the impact of changing grading workload, but the paper does not identify grading workload itself as a barrier in an ‘implementation process’ domain. (LLM, conf=0.78)
Experimental — accuracy not yet measuredDoubts about reliability and accuracy negatively impact physician confidence in the AI system.
“Finally, doubts about reliability and accuracy may negatively impact physician confidence in the system [56,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Sections 3.1.10 (Accuracy and reliability of AI technology) and 3.1.5 (Trust), with no page numbers available, report healthcare practitioners’ concerns about AI accuracy and reliability and state that AI errors could affect staff confidence. This supports the hedged link to confidence, but the paper discusses screening staff and other healthcare practitioners, not physicians specifically. (LLM, conf=0.83)
Experimental — accuracy not yet measuredDiagnostic limitations of AI systems (they may not detect other conditions besides the one for which they have been designed) are a barrier to AI adoption.
“whereas the studies of Wewetzer et al. [56] and Wahlich et al. [57] highlighted diagnostic limitations of AI systems (they may not detect other conditions besides the one for which they have been designed for, leading to incomplete diagnosis).”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Positive attitude towards the future of AI technology is a facilitator in the individuals domain.
“positive attitude towards the future of AI technology [57]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Perception of AI as a support is a facilitator in the individuals domain.
“or perception of AI as a support [57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Concerns about decision-making transparency (black box problem) are a barrier to AI adoption in diabetes management.
“Another fundamental barrier to consider is the concern about data security, liability, and how the system made the decision (black box problem) [54,55,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Whalich et al. and Held et al. were rated as medium-quality due to the absence of statements addressing the researchers' cultural positions and the mutual influence between the researchers and the research process.
“Regarding the four qualitative studies, Whalich et al. [57] and Held et al. [51] were rated as medium-quality due to the absence of statements addressing the researchers’ cultural positions and the mutual influence between the researchers and the research process.”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
Two included studies were performed inside community healthcare centers.
“Notably, two studies were performed inside community healthcare centers [52,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [57]
The Subjects, materials and methods section describes an online survey and says validation interviews took place at three diabetic eye screening programme centres. It does not identify those centres as community healthcare centres or say the study itself was performed inside them; searches for community health centres yielded no further passage establishing that setting. (LLM, conf=0.70)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Artificial intelligence in healthcare: transforming the practice of medicine
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0095 · Cited by 1,856 · see citing works
Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00132-1 · Cited by 400 · see citing works
Automated Diabetic Retinopathy Image Assessment Software
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2016.11.014 · Cited by 283 · see citing works
Self-citationFritsch, S. J., Blankenheim, A., Wahl, A., Hetfeld, P., Maaßen, O., Deffge, S., Kunze, J., Rossaint, R., Riedel, M., Marx, G., et al. (2022) · Digital Health · doi:10.1177/20552076221116772 · Cited by 255 · see citing works
Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16649 · Cited by 221 · see citing works
Keel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J., & He, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-22612-2 · Cited by 220 · see citing works
The clinical effectiveness of diabetes education models for Type 2 diabetes: a systematic review
Loveman, E., Frampton, G. K., & Clegg, A. J. (2008) · Health Technology Assessment · doi:10.3310/hta12090 · Cited by 194 · see citing works
Heydon, P., Egan, C. A., Bolter, L., Chambers, R., Anderson, J., Aldington, S. J., STRATTON, I. M., Scanlon, P. H., Webster, L., Mann, S. S., et al. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2020-316594 · Cited by 183 · see citing works
Self-citationSchünemann, H. J., Mustafa, R. A., Brożek, J., Santesso, N. A., Bossuyt, P. M., Steingart, K. R., Leeflang, M., Lange, S., Trenti, T., Langendam, M. W., et al. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.003 · Cited by 149 · see citing works
Haan, M., Ongena, Y. P., Hommes, S., Kwee, T. C., & Yakar, D. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.12.043 · Cited by 141 · see citing works
Aggarwal, R., Farag, S., Martin, G., Ashrafian, H., & Darzi, A. W. (2021) · Journal of Medical Internet Research · doi:10.2196/26162 · Cited by 135 · see citing works
Tufail, A., Kapetanakis, V. V., Salas-Vega, S., Egan, C. A., Rudisill, C., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2016) · Health Technology Assessment · doi:10.3310/hta20920 · Cited by 134 · see citing works
Self-citationLennox-Chhugani, N., Chen, Y., Pearson, V., Trzcinski, B., & James, J. J. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100293 · Cited by 70 · see citing works
Taylor‐Phillips, S., Seedat, F., Kijauskaite, G., Marshall, J., Halligan, S., Hyde, C., Given-Wilson, R., Wilkinson, L., Denniston, A. K., Glocker, B., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00088-7 · Cited by 60 · see citing works
Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening
Yap, A., Wilkinson, B., Chen, E., Han, L., Vaghefi, E., Galloway, C., & Squirrell, D. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000525 · Cited by 56 · see citing works
Willis, K., Chaudhry, U. A. R., Chandrasekaran, L., Wahlich, C., Olvera‐Barrios, A., Chambers, R., Bolter, L., Anderson, J., Barman, S. A., Fajtl, J., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-075558 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Zhelev ZPJ, Rogers M, et al. Automated grading in the diabetic eye screening programme: external review against programme appraisal criteria for the UK. In: National Screening Committee In: Committee UNS; 2021.
- Rudnicka A, et al. Evaluation of equity in performance of Artificial Intelligence for diabetic retinopathy (DR) detection. Invest Ophthalmol Vis Sci 2024;65(7):4922.
Cited by
Sorted by citations
Cangelosi, G., Conti, A., Caggianelli, G., Panella, M., Petrelli, F., Mancin, S., Ratti, M., & Masini, A. (2025) · Medicina · doi:10.3390/medicina61081403 · Cited by 14 · see citing works
“The identified facilitators are an improved clinical efficiency [52,54,55], easiness of use [52,54], and the time sparing effect, both for the patient and for the physician [53,56,57].”
InfluentialKrogh, M., Nielsen, M. G., Petersen, G. B., Jensen, M. S. A., Jensen, M. B., Vorum, H., Bruun, N. H., & Kristensen, J. K. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1610114 · Cited by 4 · see citing works
Daus, Z., Howard, M., Reeder, S. C., Foster, E., Ge, Z., Sparrow, R., & Kwan, P. (2026) · Health and Technology · doi:10.1007/s12553-026-01066-x · Cited by 2 · see citing works
Patient perceptions of artificial intelligence in ophthalmology: a cross-sectional survey study
McCarthy, A., Eltemsah, L., Cui, A., Diamond, M. E., Golebka, J. P., Thakoor, K. A., & Glass, L. R. D. (2026) · British Journal of Ophthalmology · doi:10.1136/bjo-2025-328498 · Cited by 1 · see citing works
Cohort Profile: The North East London Diabetes Study (NELDS)
Olvera‐Barrios, A., Warwick, A., Pitt, M., Schaefer, Z., Santhirasekaran, S., Chambers, R., Bolter, L., Anderson, J., Fajtl, J., Barman, S. A., et al. (2026) · International Journal of Epidemiology · doi:10.1093/ije/dyag117 · Cited by 1 · see citing works
Self-citationA Novel Approach to Retinal Blood Vessel Segmentation Using Bi-LSTM-Based Networks
Martí-Puig, P., Kapllani, K. M., & Márquez, B. A. (2025) · Mathematics · doi:10.3390/math13132043
Automated FA Leakage Measurement to Predict NPDR Progression
Virgili, G., Parravano, M., & Cushley, L. N. (2026) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2025.6096
Huang, L., Lu, H., Yang, M., Liu, Y., Wang, H., & Zhang, K. C. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1768780
Yang, X., Li, J., Xiang, Y., Ju, M., Liang, K., He, Y., Zhao, Q., & Huang, H. (2026) · doi:10.2196/preprints.99511
Donocik, W., Sałak, J., Tryczyński, P., Wrona, J., Helbin, P., Gralec, A., Ożga, S., & Spirkowicz, A. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.1(49).2026.5728
Kuppanda, P. M., Janda, M., & Caffery, L. J. (2026) · Interactive Journal of Medical Research · doi:10.2196/92969
Zhang, S.-R., Liu, S.-Y., Li, Y., Ji, D., & Li, G. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1902233
Ghanbari, M., Mirzaei, A., Barzegar, B., Mikaeilvand, N., & Nouri-Moghaddam, B. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100460
(2026) · International Journal of Drug Delivery Technology · doi:10.25258/ijddt.16.77s.20
- Authors
- Wahlich, C., Chandrasekaran, L., Chaudhry, U. A. R., Willis, K., Chambers, R., Bolter, L., Anderson, J., Shakespeare, R., Olvera-Barrios, A., Fajtl, J., Welikala, R., Barman, S., Egan, C. A., Tufail, A., Owen, C. G., & Rudnicka, A. R.
- Affiliations
- University of London (GB)
- City St George's, University of London (GB)
- Homerton University Hospital NHS Foundation Trust (GB)
- Kingston University London (GB)
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Corresponding author
- Wahlich, Charlotte
- ORCID
- Venue
- Diabetes Research and Clinical Practice
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 219
- Pages
- 111964
- ISSN
- 0168-8227 (ISSN-L)
- OpenAlex
- W4405613484
- Cited by
- 13
- References
- 17
- Citations by year
- 2026: 10 · 2025: 3
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diabetes Research and Clinical Practice (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Repository (Kingston University London) (acceptedVersion)
- St George's Online Research Archive (St George's University of London) (submittedVersion)
- Funders
- University Hospitals Birmingham NHS Foundation Trust
- Queen Elizabeth Hospital Birmingham Charity
- Diabetes Research and Wellness Foundation
- Diabetes UK
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- artificial intelligence, diabetic retinopathy, diabetic retinopathy screening, automated retinal analysis, patient perspectives, trust in AI, workforce impact, patient-practitioner relationship, data security, health disparities, diabetes screening, National Health Service
- MeSH terms
- Aged, Artificial Intelligence, Attitude of Health Personnel, Diabetic Retinopathy, England, Female, Health Personnel, Humans, Male, Mass Screening, Middle Aged, Surveys and Questionnaires, State Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
