Evidence map›Paper›PMID 38355517›Full record

ArticleBMC medical education2024

Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses.

Hui Zong, Jiakun Li, Erman Wu, Rongrong Wu, Junyu Lu, Bairong Shen

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In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 4 pooled it
–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

65 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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5 more citing papers are in PubMed but not listed here.

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.

Hui Zong *Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.ORCID http://orcid.org/0000-0002-9142-5017
Jiakun Li *Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.ORCID http://orcid.org/0000-0003-4996-9907
Erman WuDepartment of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.ORCID http://orcid.org/0000-0002-2363-3738
Rongrong WuDepartment of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.ORCID http://orcid.org/0000-0003-3876-1609
Junyu LuDepartment of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China.
Bairong ShenDepartment of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610212, China. bairong.shen@scu.edu.cn.ORCID http://orcid.org/0000-0003-2899-1531

Funding

National Natural Science Foundation of China 32270690
6 · The paper itself

Abstract

backgroundLarge language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE).

methodsWe collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions.

resultsChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening.

conclusionsThese results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

Indexed as

Artificial IntelligenceEducational MeasurementLicensureChinaData AccuracyEducation, MedicalEducation, NursingEducation, PharmacyArtificial intelligenceChatGPTMedical educationMedical examinationNatural language processing

Identifiers

PMID38355517
PMCPMC10868058

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.