Evidence map›Paper›PMID 38335017›Full record

ArticleJMIR medical education2024

Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study.

Peng Yu, Changchang Fang, Xiaolin Liu, Wanying Fu, Jitao Ling, Zhiwei Yan, Yuan Jiang, Zhengyu Cao, Maoxiong Wu, Zhiteng Chen and 6 more

Abstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. [Comparative study on the application of large language models in pre-exam learning for the Chinese dental licen-sing examination].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026
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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

16 authors.

Peng Yu *Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.ORCID 0009-0008-0345-5787
Changchang Fang *Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.ORCID 0009-0006-0794-4188
Xiaolin LiuDepartment of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.ORCID 0009-0000-4824-8820
Wanying FuDepartment of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.ORCID 0009-0003-7778-7219
Jitao LingDepartment of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.ORCID 0000-0002-1720-8620
Zhiwei YanCollege of Kinesiology, Shenyang Sport University, Shenyang, China.ORCID 0000-0002-1754-9126
Yuan JiangDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0001-8704-3676
Zhengyu CaoDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-8372-3735
Maoxiong WuDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-6943-2763
Zhiteng ChenDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-0322-323X
Wengen ZhuDepartment of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-1280-0158
Yuling ZhangDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-9071-3148
Ayiguli AbudukeremuDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0009-0004-7895-9761
Yue WangDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0009-0006-3194-5375
Xiao LiuDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-5570-289X
Jingfeng WangDepartment of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0009-0005-7309-6984

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias.

objectiveThe study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context.

methodsA data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights.

resultsOur evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response.

conclusionsAlthough ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.

Indexed as

Artificial IntelligenceClinical MedicineEducational MeasurementLanguageartificial intelligenceChatGPTChinese Postgraduate Examination for Clinical Medicineclinical decision-makingmedical caremedical educationmedical studentperformancequalitative feedback

Identifiers

PMID38335017
PMCPMC10891494

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.