Evidence map›Paper›PMID 38371244›Full record

ArticleDigital health

Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam.

Chao-Hsiung Huang, Han-Jung Hsiao, Pei-Chun Yeh, Kuo-Chen Wu, Chia-Hung Kao

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
3.6field-weighted citation impact, top 7% 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

16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.

  1. Pooled it
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  6. Using ChatGPT to Solve Clinical Radiobiology Problems.Journal of cancer education : the official journal of the American Association for Cancer Education · 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

5 authors at 2 institutions in 2 countries.

Chao-Hsiung HuangSchool of Medicine, China Medical University, Taichung.
Han-Jung HsiaoArtificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung.
Pei-Chun YehArtificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung.
Kuo-Chen WuArtificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung.
Chia-Hung KaoArtificial Intelligence Center, China Medical University Hospital, China Medical University, Taichung.ORCID https://orcid.org/0000-0002-6368-3676
China Medical University · TWNational Taiwan University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English. Methods: In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject. Result: In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology. Conclusion: ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

Indexed as

artificial intelligenceChatGPTeducational measurementOpenAITaiwanese medical licensing exam

Identifiers

PMID38371244
PMCPMC10874144
OpenAlexW4391890163

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.