Evidence map›Paper›PMID 40344200›Full record

ArticleJournal of medical Internet research2025

Differences in Expert Perspectives on AI Training in Medical Education: Secondary Analysis of a Multinational Delphi Study.

Qi Chwen Ong, Chin-Siang Ang, Nai Ming Lai, Rifat Atun, Josip Car

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Qi Chwen OngSchool of Public Health, Imperial College London, White City Campus, 90 Wood Lane, London, W12 0BZ, United Kingdom, 44 20-7589-511.ORCID 0009-0009-5621-5071
Chin-Siang AngWHO Collaborating Centre for Digital Health and Health Education, Nanyang Technological University, Singapore, Singapore.ORCID 0000-0003-1868-7827
Nai Ming LaiSchool of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia.ORCID 0000-0002-4204-3098
Rifat AtunDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States.ORCID 0000-0002-1531-5983
Josip CarSchool of Life Course and Population Sciences, King's College London, London, United Kingdom.ORCID 0000-0001-8969-371X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: In this secondary analysis of a multinational Delphi study, experts from low- and middle-income countries were less likely than those from high-income countries to consider artificial intelligence (AI) learning outcomes mandatory in preregistration medical education, potentially reflecting underlying global inequalities in medical AI education and highlighting the need for adaptable AI competency frameworks.

Indexed as

Artificial IntelligenceEducation, MedicalDelphi TechniqueHumansAIartificial intelligencecompetenciesDelphi studyglobal health educationhealth professions educationmedical education

Identifiers

PMID40344200
PMCPMC12083730

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.