ArticleBMC medical education2024
Performance of ChatGPT and Bard on the medical licensing examinations varies across different cultures: a comparison study.
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 32 papers.
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Who cites it
32 citing papers in PubMed.
- Performance of large language models on the Japanese cardiovascular surgery board examination: a comparative analysis of eight contemporary AI models with educational implications.General thoracic and cardiovascular surgery · 2026Article
- Large Language Model Performance on Multistep Clinical Cases: Comparative Study Across Question and Case Levels.JMIR medical education · 2026Article
- [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 · 2026Article
- Bloom's taxonomy-based comparison of artificial intelligence and dental students in restorative dentistry.BMC medical education · 2026Article
- Comparative Performance of State-of-the-Art LLMs on the KDLE: A 2025 Benchmark Study.International dental journal · 2026Article
- Performance of Large Language Models on the Brazilian National Medical Education Examination: Comparative Benchmark Study.JMIR medical education · 2026Article
- Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.Scientific reports · 2026Article
- A Comparative Analysis of GPT-4o and ERNIE Bot in a Chinese Radiation Oncology Exam.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Performance of 5 AI Models on United States Medical Licensing Examination Step 1 Questions: Comparative Observational Study.JMIR AI · 2026Article
- Performance of Large Language Models on Cognitive Aptitude Testing: A Multi-Run Evaluation on the German Medical School Admission Test (TMS).European journal of investigation in health, psychology and education · 2026Article
- Large language models in Chinese anesthesiology residency examinations: a comparative analysis of performance, reliability and clinical reasoning.BMC medical education · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Large language models as data-driven engines for benchmarking preventive and clinical knowledge in Chinese dental examinations.Frontiers in oral health · 2026Article
- Safety of a large language model-based clinical decision support system in African primary healthcare.Nature health · 2026Article
- Assessing multiple-choice question quality in internal medicine: a comparative analysis of three large language models against expert consensus.Frontiers in medicine · 2026Article
- Benchmarking large language models on a Chinese radiation oncology technology examination-preparation question set: accuracy, consensus, and efficiency for AI assisted education.Frontiers in oncology · 2026Article
- Assessing accuracy and legitimacy of multimodal large language models on Japan Diagnostic Radiology Board Examination.Japanese journal of radiology · 2026Article
- When AI models take the exam: large language models vs medical students on multiple-choice course exams.Medical education online · 2025Article
- Article
- Evaluating the Performance of DeepSeek-R1 and DeepSeek-V3 Versus OpenAI Models in the Chinese National Medical Licensing Examination: Cross-Sectional Comparative Study.JMIR medical education · 2025Article
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9 authors.
Funding
Abstract
backgroundThis study aimed to evaluate the performance of GPT-3.5, GPT-4, GPT-4o and Google Bard on the United States Medical Licensing Examination (USMLE), the Professional and Linguistic Assessments Board (PLAB), the Hong Kong Medical Licensing Examination (HKMLE) and the National Medical Licensing Examination (NMLE).
methodsThis study was conducted in June 2023. Four LLMs (Large Language Models) (GPT-3.5, GPT-4, GPT-4o and Google Bard) were applied to four medical standardized tests (USMLE, PLAB, HKMLE and NMLE). All questions are multiple-choice questions and were sourced from the question banks of these examinations.
resultsIn USMLE step 1, step 2CK and Step 3, there are accuracy rates of 91.5%, 94.2% and 92.7% provided from GPT-4o, 93.2%, 95.0% and 92.0% provided from GPT-4, 65.6%, 71.6% and 68.5% provided from GPT-3.5, and 64.3%, 55.6%, 58.1% from Google Bard, respectively. In PLAB, HKMLE and NMLE, GPT-4o scored 93.3%, 91.7% and 84.9%, GPT-4 scored 86.7%, 89.6% and 69.8%, GPT-3.5 scored 80.0%, 68.1% and 60.4%, and Google Bard scored 54.2%, 71.7% and 61.3%. There was significant difference in the accuracy rates of four LLMs in the four medical licensing examinations.
conclusionGPT-4o performed better in the medical licensing examinations than other three LLMs. The performance of the four models in the NMLE examination needs further improvement. CLINICAL TRIAL NUMBER: Not applicable.
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