Evidence map›Paper›PMID 42284608›Full record

ArticleJMIR medical education2026

AI in UK Medical Education: A Framework for Curriculum Reform.

Aditya Gaur, Joecelyn Kirani Tan, Medha Sridhar Rao, Muhtasim Fuad, Taha Bhatti, Hareesha Rishab Bharadwaj, Khabab Abbasher Hussien Mohamed Ahmed

Abstract read
In one paragraph

Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Aditya GaurYeovil District Hospital, Somerset NHS Foundation Trust, Yeovil, United Kingdom.
Joecelyn Kirani TanFaculty of Biology, Medicine, and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 161 306 6000.
Medha Sridhar RaoSchool of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom.
Muhtasim FuadFaculty of Biology, Medicine, and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 161 306 6000.
Taha BhattiFaculty of Biology, Medicine, and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 161 306 6000.
Hareesha Rishab BharadwajFaculty of Biology, Medicine, and Health, University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 161 306 6000.
Khabab Abbasher Hussien Mohamed AhmedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.ORCID 0000-0003-4608-5321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Artificial intelligence (AI) is increasingly transforming health care through improvements in diagnosis, predictive analytics, and workflow optimization. However, there remains a significant gap in AI training within UK medical education, leaving future clinicians underprepared for AI-driven health care environments. This viewpoint paper investigated global best practices for AI integration into medical education and proposes a structured framework for embedding AI into the UK medical curriculum. It aimed to assess current attitudes, highlight existing knowledge gaps, and recommend practical implementation strategies. An analysis of international case studies (eg, Stanford University, the University of Toronto, and Chinese University of Hong Kong) was conducted alongside a review of teaching methodologies, stakeholder perspectives, and UK-based surveys to identify core competencies and challenges in AI education. Effective integration strategies include the use of AI-powered simulations, interdisciplinary collaboration, elective modules, and faculty training. Major barriers include lack of AI-literate educators, insufficient ethical training, and limited infrastructure. Knowledge gaps persist among students and faculty in areas such as algorithmic bias, AI ethics, and clinical decision-making. To meet the demands of modern health care, the UK medical curriculum must adopt comprehensive AI training. This includes practical exposure, ethical awareness, and stakeholder engagement. Proactive reform will ensure that graduates are equipped to critically and ethically apply AI tools in clinical practice.

Indexed as

Artificial IntelligenceCurriculumEducation, MedicalHumansUnited KingdomAIAI ethicsAI literacyartificial intelligenceartificial intelligence ethicsartificial intelligence literacycurriculum reformdigital healthinterdisciplinary trainingmedical educationmedical studentssimulation-based learningUK health care

Identifiers

PMID42284608
PMCPMC13263032

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.