Evidence map›Paper›PMID 40611478›Full record

ArticleJMIR AI2025

Evaluation of ChatGPT Performance on Emergency Medicine Board Examination Questions: Observational Study.

Mila Pastrak, Sten Kajitani, Anthony James Goodings, Austin Drewek, Andrew LaFree, Adrian Murphy

Abstract read
In one paragraph

Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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

6 authors.

Mila Pastrak *School of Medicine, University College Cork, Cork, Ireland.ORCID http://orcid.org/0009-0005-4423-8197
Sten Kajitani *School of Medicine, University College Cork, Cork, Ireland.ORCID http://orcid.org/0009-0007-5752-6432
Anthony James GoodingsSchool of Medicine, University College Cork, Cork, Ireland.ORCID http://orcid.org/0009-0005-9702-0968
Austin DrewekDepartment of Emergency Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID http://orcid.org/0009-0002-1336-9459
Andrew LaFreeDepartment of Emergency Medicine, University of California, San Diego, 200 W. Arbor Dr. #8676, San Diego, CA, 92103, United States, 1 6198319501.ORCID http://orcid.org/0000-0002-4817-1664
Adrian MurphySchool of Medicine, University College Cork, Cork, Ireland.ORCID http://orcid.org/0000-0002-9417-3580

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The ever-evolving field of medicine has highlighted the potential for ChatGPT as an assistive platform. However, its use in medical board examination preparation and completion remains unclear. Objective: This study aimed to evaluate the performance of a custom-modified version of ChatGPT-4, tailored with emergency medicine board examination preparatory materials (Anki flashcard deck), compared to its default version and previous iteration (3.5). The goal was to assess the accuracy of ChatGPT-4 answering board-style questions and its suitability as a tool to aid students and trainees in standardized examination preparation. Methods: A comparative analysis was conducted using a random selection of 598 questions from the Rosh In-Training Examination Question Bank. The subjects of the study included three versions of ChatGPT: the Default, a Custom, and ChatGPT-3.5. The accuracy, response length, medical discipline subgroups, and underlying causes of error were analyzed. Results: The Custom version did not demonstrate a significant improvement in accuracy over the Default version (P=.61), although both significantly outperformed ChatGPT-3.5 (P<.001). The Default version produced significantly longer responses than the Custom version, with the mean (SD) values being 1371 (444) and 929 (408), respectively (P<.001). Subgroup analysis revealed no significant difference in the performance across different medical subdisciplines between the versions (P>.05 in all cases). Both the versions of ChatGPT-4 had similar underlying error types (P>.05 in all cases) and had a 99% predicted probability of passing while ChatGPT-3.5 had an 85% probability. Conclusions: The findings suggest that while newer versions of ChatGPT exhibit improved performance in emergency medicine board examination preparation, specific enhancement with a comprehensive Anki flashcard deck on the topic does not significantly impact accuracy. The study highlights the potential of ChatGPT-4 as a tool for medical education, capable of providing accurate support across a wide range of topics in emergency medicine in its default form.

Indexed as

artificial intelligenceChatGPT-4emergency medicineexaminationexamination preparationmedical education

Identifiers

PMID40611478
PMCPMC12231519

What OpenQuestion holds

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Registered trials

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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.