Evidence map›Paper›PMID 38596832›Full record

ArticleJMIR medical education2024

Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study.

Alexandre Hudon, Barnabé Kiepura, Myriam Pelletier, Véronique Phan

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
14.1field-weighted citation impact, top 1% of its field
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

15 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
  2. Artificial Intelligence-Assisted Generation of Case Scenarios and Multiple-Choice Questions in Psychiatry: A Pilot Study.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
  3. Strengths and Potential Pitfalls of the Use of Artificial Intelligence in Psychiatric Education and Practice.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
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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

4 authors at 2 institutions in 1 country.

Alexandre HudonDepartment of Psychiatry and Addictology, University of Montreal, Montreal, QC, Canada.ORCID 0000-0002-4868-0928
Barnabé KiepuraDepartment of Psychiatry and Addictology, University of Montreal, Montreal, QC, Canada.ORCID 0009-0000-6345-8578
Myriam PelletierFaculty of Medicine, Université Laval, Québec, QC, Canada.ORCID 0009-0008-6779-1018
Véronique PhanDepartment of Pediatrics, Université de Montréal, Montreal, QC, Canada.ORCID 0000-0001-5391-1578
Université de Montréal · CAUniversité Laval · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)-based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials. Objective: The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion. Methods: This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs. Results: A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content. Conclusions: This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy.

Indexed as

Education, Medical, UndergraduatePsychiatryArtificial IntelligenceCanadaHumansLearningaccuracyartificial intelligenceChatGPTclinical questionsconcordance scriptsdesigneducationeducationaleducatorsevaluationlearningmachine learningmedical educationmedical learnerspsychiatrysupportteachingtoolvalidation

Identifiers

PMID38596832
PMCPMC11007379
OpenAlexW4394689344

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.