TY  - JOUR
AU  - Liu, Mingxin
AU  - Okuhara, Tsuyoshi
AU  - Chang, XinYi
AU  - Shirabe, Ritsuko
AU  - Nishiie, Yuriko
AU  - Okada, Hiroko
AU  - Kiuchi, Takahiro
PY  - 2024
DA  - 2024/7/25
TI  - Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis
JO  - J Med Internet Res
SP  - e60807
VL  - 26
KW  - large language model, ChatGPT, medical licensing examination, medical education
KW  - LLMs
KW  - NLP
KW  - natural language processing
KW  - artificial intelligence
KW  - language models
KW  - review methods
KW  - systematic
KW  - meta-analysis
AB  - Background: Over the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT’s performance on different medical licensing examinations. Objective: In this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education. Methods: We searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses. Results: A total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5’s performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non–English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5’s (P=.03) and GPT-4’s (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT’s accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs. Conclusions: GPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education. Trial Registration: PROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e60807
UR  - https://doi.org/10.2196/60807
DO  - 10.2196/60807
ID  - info:doi/10.2196/60807
ER  -