Evidence map›Paper›PMID 40144083›Full record

ReviewMedical science educator2025

ChatGPT and Other Large Language Models in Medical Education - Scoping Literature Review.

Alexandra Aster, Matthias Carl Laupichler, Tamina Rockwell-Kollmann, Gilda Masala, Ebru Bala, Tobias Raupach

Abstract readReview
In one paragraph

Review in Medical science educator, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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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

6 authors.

Alexandra Aster *Institute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.ORCID 0000-0003-0407-3250
Matthias Carl Laupichler *Institute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.ORCID 0000-0003-3104-1123
Tamina Rockwell-KollmannInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Gilda MasalaInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Ebru BalaInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Tobias RaupachInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.ORCID 0000-0003-2555-8097

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review aims to provide a summary of all scientific publications on the use of large language models (LLMs) in medical education over the first year of their availability. A scoping literature review was conducted in accordance with the PRISMA recommendations for scoping reviews. Five scientific literature databases were searched using predefined search terms. The search yielded 1509 initial results, of which 145 studies were ultimately included. Most studies assessed LLMs' capabilities in passing medical exams. Some studies discussed advantages, disadvantages, and potential use cases of LLMs. Very few studies conducted empirical research. Many published studies lack methodological rigor. We therefore propose a research agenda to improve the quality of studies on LLM.

Indexed as

Artificial intelligenceChatGPTGenerative AILarge language modelMedical education

Identifiers

PMID40144083
PMCPMC11933646

What OpenQuestion holds

Textmetadata
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.