Evidence map›Paper›PMID 42163311›Full record

ArticleBMC medical education2026

Artificial intelligence and clinical informatics in UK ophthalmology training: a national cross-sectional survey.

Thomas Nash, Rishi Ramessur, Alan Roy Abraham, Peter Thomas

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Thomas NashMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Rishi RamessurMoorfields Eye Hospital NHS Foundation Trust, London, UK. rishi.ramessur@nhs.net.
Alan Roy AbrahamBristol Eye Hospital, Bristol, UK.
Peter ThomasMoorfields Eye Hospital NHS Foundation Trust, London, UK.

Funding

UKRI Open Access Fund, University of Edinburgh UKRI AI Centre for Doctoral Training in Biomedical Innovation EP/Y009460/1
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) and clinical informatics (CI) are strategic priorities for UK ophthalmology, with the Royal College of Ophthalmologists identifying education and training as central to safe implementation. However, formal CI/AI training is not currently embedded within the Ophthalmic Specialty Training (OST) curriculum. This study aimed to evaluate trainees' baseline knowledge, access to training and perceived educational needs to inform future curriculum development.

methodsWe conducted a national cross-sectional online survey of UK ophthalmology trainees between January and June 2025. A bespoke questionnaire was developed in accordance with the CHERRIES framework and informed by national policy and the NHS England DART-Ed digital competency framework. Content validity was strengthened through expert review by members of the Royal College of Ophthalmologists Informatics Working Group. The anonymised survey assessed baseline knowledge, perceived clinical relevance, access to training, educational preferences and career intentions. Quantitative data were analysed descriptively and free-text responses underwent inductive thematic analysis.

resultsSeventy-one trainees responded, representing all UK training deaneries and all training grades. Most reported only basic or no knowledge of CI (76%) or AI (76%), with limited access to deanery-level training. Support for formal education was high (CI 70%; AI 80%). Nearly half (49%) wished to incorporate CI/AI into future consultant roles. Trainees favoured blended, clinically relevant training models combining online learning, regional teaching and supervised project work. Qualitative themes emphasised foundational literacy, governance and safety, practical application and flexible delivery.

conclusionsUK ophthalmology trainees demonstrate majority support for structured CI/AI education, with over 70% supporting formal training in both domains, despite limited current provision. These findings support integration of digital competencies within the OST curriculum, aligned with national frameworks, to prepare the future workforce for safe and effective AI-enabled ophthalmic care.

Indexed as

Artificial IntelligenceMedical InformaticsOphthalmologyCross-Sectional StudiesCurriculumEducation, Medical, GraduateFemaleHumansMaleSurveys and QuestionnairesUnited KingdomArtificial intelligenceClinical informaticsDigital healthOphthalmology Medical educationPostgraduate training

Identifiers

PMID42163311
PMCPMC13491797

What OpenQuestion holds

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LicenceCC BY
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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.