Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study.
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ABSTRACT: Background
The competence of ChatGPT (Chat Generative Pre-Trained Transformer) in non-English languages is not well studied.Objective
This study compared the performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of these models for clinical reasoning and medical knowledge in non-English languages.Methods
This study used the default mode of ChatGPT, which is based on GPT-3.5; the GPT-4 model of ChatGPT Plus; and the 117th JMLE in 2023. A total of 254 questions were included in the final analysis, which were categorized into 3 types, namely general, clinical, and clinical sentence questions.Results
The results indicated that GPT-4 outperformed GPT-3.5 in terms of accuracy, particularly for general, clinical, and clinical sentence questions. GPT-4 also performed better on difficult questions and specific disease questions. Furthermore, GPT-4 achieved the passing criteria for the JMLE, indicating its reliability for clinical reasoning and medical knowledge in non-English languages.Conclusions
GPT-4 could become a valuable tool for medical education and clinical support in non-English-speaking regions, such as Japan.
SUBMITTER: Takagi S
PROVIDER: S-EPMC10365615 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
Publications
Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study.
Takagi Soshi S Watari Takashi T Erabi Ayano A Sakaguchi Kota K
JMIR medical education 20230629
<h4>Background</h4>The competence of ChatGPT (Chat Generative Pre-Trained Transformer) in non-English languages is not well studied.<h4>Objective</h4>This study compared the performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of these models for clinical reasoning and medical knowledge in non-English languages.<h4>Methods</h4>This study used the default mode of ChatGPT, which is based on GPT-3.5; ...[more]
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Project description:The remarkable performance of ChatGPT, launched in November 2022, has significantly impacted the field of natural language processing, inspiring the application of large language models as supportive tools in clinical practice and research worldwide. Although GPT-3.5 recently scored high on the United States Medical Licensing Examination, its performance on medical licensing examinations of other nations, especially non-English speaking nations, has not been sufficiently evaluated. This study assessed GPT's performance on the National Medical Licensing Examination (NMLE) in Japan and compared it with the actual minimal passing rate for this exam. In particular, the performances of both the GPT-3.5 and GPT-4 models were considered for the comparative analysis. We initially used the GPT models and several prompts for 290 questions without image data from the 116th NMLE (held in February 2022 in Japan) to maximize the performance for delivering correct answers and explanations of the questions. Thereafter, we tested the performance of the best GPT model (GPT-4) with optimized prompts on a dataset of 262 questions without images from the latest 117th NMLE (held in February 2023). The best model with the optimized prompts scored 82.7% for the essential questions and 77.2% for the basic and clinical questions, both of which sufficed the minimum passing scoring rates of 80.0% and 74.6%, respectively. After an exploratory analysis of 56 incorrect answers from the model, we identified the three major factors contributing to the generation of the incorrect answers-insufficient medical knowledge, information on Japan-specific medical system and guidelines, and mathematical errors. In conclusion, GPT-4 with our optimized prompts achieved a minimum passing scoring rate in the latest 117th NMLE in Japan. Beyond its original design of answering examination questions for humans, these artificial intelligence (AI) models can serve as one of the best "sidekicks" for solving problems and addressing the unmet needs in the medical and healthcare fields.
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