Evidence map›Paper›PMID 42129234›Full record

ArticleScientific reports2026

ChatGPT as a tool for reviewing multiple-choice questions in the health sector.

Tatiane Iembo, Helena Landim Gonçalves Cristóvão, Patrícia Carla Zanelatto Gonçalves, Wagner Ricardo Montor, Patrícia da Silva Fucuta, Toufic Anbar Neto, Júlio César André, Milton de Arruda Martins

Abstract read
In one paragraph

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

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

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

8 authors.

Tatiane IemboFaculty of Medicine in São José Do Rio Preto (FACERES), São José Do Rio Preto, 15090-305, Brazil. iembo.tatiane@gmail.com.
Helena Landim Gonçalves CristóvãoCenter for Studies and Development of Health Education - Faculty of Medicine of São José Do Rio Preto (CEDES / FAMERP), São José Do Rio Preto, 15090-000, Brazil.
Patrícia Carla Zanelatto GonçalvesMackenzie Evangelical College of Paraná (MACKENZIE), Curitiba, 80730-000, Brazil.
Wagner Ricardo MontorFaculty of Medical Sciences of Santa Casa de São Paulo (FCMSCSP), São Paulo, 01224-001, Brazil.
Patrícia da Silva FucutaFaculty of Medicine in São José Do Rio Preto (FACERES), São José Do Rio Preto, 15090-305, Brazil.
Toufic Anbar NetoFaculty of Medicine in São José Do Rio Preto (FACERES), São José Do Rio Preto, 15090-305, Brazil.
Júlio César AndréCenter for Studies and Development of Health Education - Faculty of Medicine of São José Do Rio Preto (CEDES / FAMERP), São José Do Rio Preto, 15090-000, Brazil.
Milton de Arruda MartinsCenter for the Development of Medical Education (CEDEM), Faculty of Medicine of the University of São Paulo (FMUSP), São Paulo, 01246-903, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI), particularly ChatGPT-4, offers promising applications in medical education, including multiple-choice question (MCQ) development. This study aimed to evaluate and compare the quality of 36 MCQs created by medical faculty with their versions reviewed by ChatGPT-4. A cross-sectional, quantitative approach was used. Ten external health education specialists and four study authors (internal evaluators) assessed the questions based on 38 criteria. While external evaluators found no statistically significant difference in criteria met between versions (p = 0.325), the study authors, who underwent standardization meetings, identified a statistically significant increase in the number of criteria met by ChatGPT-4-reviewed MCQs (p < 0.001). Descriptive statistics, Wilcoxon Signed-Rank Test, and Non-Metric Multidimensional Scaling were employed. The results showed that ChatGPT-4 demonstrated proficiency in modifying questions to reflect greater structural clarity and adherence to basic item-writing principles, resulting in questions with increased clarity and objectivity. However, it struggled to incorporate clinical reasoning and higher-order thinking when these were lacking, particularly given the non-optimized prompt used. Despite these limitations, AI's revisions were aligned with faculty quality standards, demonstrating its potential to complement faculty efforts, emphasizing the critical role of calibrated human expertise and effective prompt engineering, rather than replacement.

Indexed as

Artificial IntelligenceEducational MeasurementEducation, MedicalCross-Sectional StudiesGenerative Artificial IntelligenceHumansEducational assessmentEducational technologyExam questionMedical education

Identifiers

PMID42129234
PMCPMC13365500

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.