Evidence map›Paper›PMID 40607498›Full record

ArticleJMIR AI2025

Comparative Performance of Medical Students, ChatGPT-3.5 and ChatGPT-4.0 in Answering Questions From a Brazilian National Medical Exam: Cross-Sectional Questionnaire Study.

Mateus Rodrigues Alessi, Heitor Augusto Gomes, Gabriel Oliveira, Matheus Lopes de Castro, Fabiano Grenteski, Leticia Miyashiro, Camila do Valle, Leticia Tozzini Tavares da Silva, Cristina Okamoto

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

9 authors.

Mateus Rodrigues AlessiSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0000-0001-7612-8009
Heitor Augusto GomesSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0000-0001-8140-6536
Gabriel OliveiraSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0009-0001-6472-840X
Matheus Lopes de CastroSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0009-0009-9888-3131
Fabiano GrenteskiSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0009-0004-2508-4279
Leticia MiyashiroSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0009-0006-2683-8479
Camila do ValleSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0000-0003-4863-9578
Leticia Tozzini Tavares da SilvaSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0000-0003-0831-252X
Cristina OkamotoSchool of Medicine, Universidade Positivo, R. Prof. Pedro Viriato Parigot de Souza, 5300, Curitiba, 81280-330, Brazil, (41) 3317-3010.ORCID http://orcid.org/0000-0003-3112-7453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence has advanced significantly in various fields, including medicine, where tools like ChatGPT (GPT) have demonstrated remarkable capabilities in interpreting and synthesizing complex medical data. Since its launch in 2019, GPT has evolved, with version 4.0 offering enhanced processing power, image interpretation, and more accurate responses. In medicine, GPT has been used for diagnosis, research, and education, achieving significant milestones like passing the United States Medical Licensing Examination. Recent studies show that GPT 4.0 outperforms earlier versions and even medical students on medical exams. Objective: This study aimed to evaluate and compare the performance of GPT versions 3.5 and 4.0 on Brazilian Progress Tests (PT) from 2021 to 2023, analyzing their accuracy compared to medical students. Methods: A cross-sectional observational study was conducted using 333 multiple-choice questions from the PT, excluding questions with images and those nullified or repeated. All questions were presented sequentially without modification to their structure. The performance of GPT versions was compared using statistical methods and medical students' scores were included for context. Results: There was a statistically significant difference in total performance scores across the 2021, 2022, and 2023 exams between GPT-3.5 and GPT-4.0 (P=.03). However, this significance did not remain after Bonferroni correction. On average, GPT v3.5 scored 68.4%, whereas v4.0 achieved 87.2%, reflecting an absolute improvement of 18.8% and a relative increase of 27.4% in accuracy. When broken down by subject, the average scores for GPT-3.5 and GPT-4.0, respectively, were as follows: surgery (73.5% vs 88.0%, P=.03), basic sciences (77.5% vs 96.2%, P=.004), internal medicine (61.5% vs 75.1%, P=.14), gynecology and obstetrics (64.5% vs 94.8%, P=.002), pediatrics (58.5% vs 80.0%, P=.02), and public health (77.8% vs 89.6%, P=.02). After Bonferroni correction, only basic sciences and gynecology and obstetrics retained statistically significant differences. Conclusions: GPT-4.0 demonstrates superior accuracy compared to its predecessor in answering medical questions on the PT. These results are similar to other studies, indicating that we are approaching a new revolution in medicine.

Indexed as

academic performanceaccuracyAIartificial intelligencebiomedical technologyChatGPTethicsexam questionsintelligent systemsmedical datamedical educationmedical ethicsmedical exammedical schoolmedical studentobservational study

Identifiers

PMID40607498
PMCPMC12223693

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.