Evidence map›Paper›PMID 37768724›Full record

ArticleJMIR medical education2023

Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study.

Javier A Flores-Cohaila, Abigaíl García-Vicente, Sonia F Vizcarra-Jiménez, Janith P De la Cruz-Galán, Jesús D Gutiérrez-Arratia, Blanca Geraldine Quiroga Torres, Alvaro Taype-Rondan

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 6 pooled it
3.3field-weighted citation impact, top 6% 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

42 citing papers in PubMed, 6 syntheses or guidelines pooled it, 88 citations in OpenAlex.

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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

7 authors at 5 institutions in 1 country.

Javier A Flores-CohailaAcademic Department, USAMEDIC, Lima, Peru.ORCID 0000-0002-9169-2094
Abigaíl García-VicenteSchool of Medicine, Universidad Nacional de Piura, Piura, Peru.ORCID 0000-0003-3679-4239
Sonia F Vizcarra-JiménezComité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, Lima, Peru.ORCID 0000-0002-8030-4092
Janith P De la Cruz-GalánComité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, Lima, Peru.ORCID 0000-0001-7261-9038
Jesús D Gutiérrez-ArratiaAcademic Department, USAMEDIC, Lima, Peru.ORCID 0000-0003-4082-5966
Blanca Geraldine Quiroga TorresComité Permanente Académico, Sociedad Científica Médico Estudiantil Peruana, Lima, Peru.ORCID 0000-0002-1141-1062
Alvaro Taype-RondanUnidad de Investigación Para la Generación y Síntesis de Evidencias en Salud, Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru.ORCID 0000-0001-8758-0463
Universidad Científica del Sur · PEUniversidad de San Martín de Porres · PEUniversidad Nacional de Piura · PEUniversidad Privada de Tacna · PEUniversidad San Ignacio de Loyola · PE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT has shown impressive performance in national medical licensing examinations, such as the United States Medical Licensing Examination (USMLE), even passing it with expert-level performance. However, there is a lack of research on its performance in low-income countries' national licensing medical examinations. In Peru, where almost one out of three examinees fails the national licensing medical examination, ChatGPT has the potential to enhance medical education.

objectiveWe aimed to assess the accuracy of ChatGPT using GPT-3.5 and GPT-4 on the Peruvian National Licensing Medical Examination (Examen Nacional de Medicina [ENAM]). Additionally, we sought to identify factors associated with incorrect answers provided by ChatGPT.

methodsWe used the ENAM 2022 data set, which consisted of 180 multiple-choice questions, to evaluate the performance of ChatGPT. Various prompts were used, and accuracy was evaluated. The performance of ChatGPT was compared to that of a sample of 1025 examinees. Factors such as question type, Peruvian-specific knowledge, discrimination, difficulty, quality of questions, and subject were analyzed to determine their influence on incorrect answers. Questions that received incorrect answers underwent a three-step process involving different prompts to explore the potential impact of adding roles and context on ChatGPT's accuracy.

resultsGPT-4 achieved an accuracy of 86% on the ENAM, followed by GPT-3.5 with 77%. The accuracy obtained by the 1025 examinees was 55%. There was a fair agreement (κ=0.38) between GPT-3.5 and GPT-4. Moderate-to-high-difficulty questions were associated with incorrect answers in the crude and adjusted model for GPT-3.5 (odds ratio [OR] 6.6, 95% CI 2.73-15.95) and GPT-4 (OR 33.23, 95% CI 4.3-257.12). After reinputting questions that received incorrect answers, GPT-3.5 went from 41 (100%) to 12 (29%) incorrect answers, and GPT-4 from 25 (100%) to 4 (16%).

conclusionsOur study found that ChatGPT (GPT-3.5 and GPT-4) can achieve expert-level performance on the ENAM, outperforming most of our examinees. We found fair agreement between both GPT-3.5 and GPT-4. Incorrect answers were associated with the difficulty of questions, which may resemble human performance. Furthermore, by reinputting questions that initially received incorrect answers with different prompts containing additional roles and context, ChatGPT achieved improved accuracy.

Indexed as

Clinical CompetenceEducational MeasurementLicensure, MedicalAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMalePeruAIartificial intelligenceassessmentChatGPTENAMExamen Nacional de Medicinagenerative pre-trained transformerlearning modellicensing examinationmedical educationmedical examinationPeru

Identifiers

PMID37768724
PMCPMC10570896
OpenAlexW4386439692

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.