Evidence map›Paper›PMID 39557926›Full record

ArticleScientific reports2024

Comparing the performance of ChatGPT-3.5-Turbo, ChatGPT-4, and Google Bard with Iranian students in pre-internship comprehensive exams.

Soolmaz Zare, Soheil Vafaeian, Mitra Amini, Keyvan Farhadi, Mohammadreza Vali, Ali Golestani

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2024. 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

6 authors.

Soolmaz ZareClinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Soheil VafaeianSchool of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. soheylvafa@gmail.com.ORCID 0009-0004-8256-376X
Mitra AminiClinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Keyvan FarhadiSchool of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Mohammadreza ValiSchool of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Ali GolestaniNon-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

Funding

Shiraz University of Medical Sciences 27250
6 · The paper itself

Abstract

This study aims to measure the performance of different AI-language models in three sets of pre-internship medical exams and to compare their performance with Iranian medical students. Three sets of Persian pre-internship exams were used, along with their English translation (six sets in total). In late September 2023, we sent requests to ChatGPT-3.5-Turbo-0613, GPT-4-0613, and Google Bard in both Persian and English languages (excluding questions with any visual content) with each query in a new session and reviewed their responses. GPT models produced responses at varying levels of randomness. In both Persian and English tests, GPT-4 ranked first and obtained the highest score in all exams and different levels of randomness. While Google Bard scored below average on the Persian exams (still in an acceptable range), ChatGPT-3.5 failed all exams. There was a significant difference between the Large Language Models (LLMs) in Persian exams. While GPT-4 yielded the best scores on the English exams, the distinction between all LLMs and students was not statistically significant. The GPT-4 model outperformed students and other LLMs in medical exams, highlighting its potential application in the medical field. However, more research is needed to fully understand and address the limitations of using these models.

Indexed as

Students, MedicalEducational MeasurementHumansInternship and ResidencyIranLanguageArtificial intelligenceChatGPTGoogle BardLarge Language ModelMedical students

Identifiers

PMID39557926
PMCPMC11574257

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.