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This paper is in the following e-collection/theme issue:

Artificial Intelligence (AI) in Medical Education (913) e-Learning and Digital Medical Education (1694) eHealth Literacy / Digital Literacy (939) Testing and Assessment in Medical Education (250) Chatbots and Conversational Agents (1259) Artificial Intelligence (5125) Generative Language Models Including ChatGPT (1690)

Published on in Vol 12 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81399, first published .
Medical students in a modern classroom using laptops and studying

Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study

Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study

Authors of this article:

Livia Maria Strasser1 Author Orcid Image ;   Wilma Anschuetz2 Author Orcid Image ;   Fabio Dennstädt1, 3 Author Orcid Image ;   Janna Hastings1, 4, 5 Author Orcid Image
Article Authors Cited by (18) Tweetations Metrics

Journals

  1. Kondo T, Donkers J, Nishigori H, Rovers S, Heeneman S. AI-Generated Versus Human Supervisor Feedback on Medical Students’ Clinical Clerkship Logs: Cross-Sectional Convergent Mixed Methods Study. JMIR Medical Education 2026;12:e90064 View
  2. Siebielec J, Raciborski F. Assessment of vaccine information accuracy across large language models. Frontiers in Public Health 2026;14 View
  3. Xu C, Chen Y, Skelding S, Wang D, Zhang Q, Schmölzer G, Cheung P. Performance of large language models in neonatal resuscitation assessments versus healthcare providers: an exploratory study. Frontiers in Artificial Intelligence 2026;9 View
  4. Huang C, Sun Y, Liu W. A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination. Frontiers in Medicine 2026;13 View
  5. Takhdat K, El fadely A, Mohamed E, Ouaamr A, El Adib A. A Systematic Review of Generative Artificial Intelligence-powered Healthcare Simulation for Clinical Reasoning Skills Development: Applications, Outcomes and Challenges. Medical Science Educator 2026 View
  6. Zhou Q, Jia Y, Hu H, Huang D, Chen X, Xia Y, Wu W. Performance of Large Language Models for Oncology Nursing Decision Support: Cross-Sectional Study. Journal of Medical Internet Research 2026;28:e97802 View
  7. Güler I, Kraus A, Grieb G, Cambaz U, Stelling H, Demirdover C. Artificial Intelligence in Plastic Surgery Education: Insights from Parallel Turkish and English Versions of a Board Examination. Applied Sciences 2026;16(15):7559 View
  8. Jannah S, Nishiyama T, Peng S, Wakamiya S, Aramaki E. Multilingual Disparities in Large Language Model–Based Symptom Detection for Global Disease Surveillance: Evaluation Study. JMIR AI 2026;5:e103321 View
  9. Qiang W, Deng X, Hou T, Qiang L. AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review. Frontiers in Medicine 2026;13 View
  10. Cho A, Eo S, Jeon M, Kim J, Ihm J, Seo D. Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians. JMIR Medical Education 2026;12:e91809 View
  11. Wu Z, Xu H, Feng F, Zhang S, Cheng R, Shi T, Wang S, Li Y. Bilingual Performance of Large Language Models in Answering Consumer Health Questions in English and Chinese: Comparative Benchmark Study. Journal of Medical Internet Research 2026;28:e96587 View
  12. Alkhanishvili Z, Gogilashvili K, Gvasalia N, Samkharadze S, Lursmanashvili L, Nachkebia L, Chkhikvishvili A, Tahir A, Tsitskishvili N. Guideline Concordance of Antibiotic Recommendations From Large Language Models in Dentistry: A Vignette-Based Evaluation in Georgian and English. Cureus 2026 View
  13. Wang H, Lu Y, Nan K, Zhang Q, Wang J, Qiu L. Quality Assessment of Artificial Intelligence Chatbot Responses to Postearthquake Health Frequently Asked Questions Across Platforms, Languages, and Generation Modes: Comparative Study (Preprint). JMIR AI 2026 View
  14. Chidebe R, Prasad Sapkota K, Ipiankama S, Ejemole D, Leibel L, Banwo F, de Moraes F. Comparing AI-ChatGPT and clinician-authored cancer patient education materials: A cross-sectional assessment. Journal of Clinical and Translational Science 2026;10(1) View
  15. Jo E, Lee Y, Cho E, Kim S, Park E, Ryu H, Joo H, Song S. Development and Evaluation of an Instruction-Tuned Large Language Model for Reply Letter Generation from Multiple Clinical Document Types. Journal of Medical Systems 2026;50(1) View
  16. Cai Q, Bi K, Zhang X, Ye Z, Chen G, Wang Z, An Z, Fu W. Evaluation of Large Language Models for Emergency Triage in Urology: A Multi-Model Study. Diagnostics 2026;16(21):3510 View
  17. Cao S, Liang J, Zhang H, Cui X, Zhao Q, Cheng Y, Ren Y, Jiang Y, Li H. Cross-linguistic differences in responses to patient queries on osteonecrosis of the femoral head between English and Chinese-oriented large language models: a semantically matched bilingual cross-sectional study. Frontiers in Public Health 2026;14 View
  18. Bantidos M, Dafli E, Karagiannidis E, Fyntanidou B, Gilou S, Nasoufidou A, Bamidis P. Perceived Ratings of AI-Generated and Expert-Reviewed vs Clinician-Developed Virtual Cardiology Patient Scenarios Among Medical Students: Randomized Controlled Study. JMIR Medical Education 2026;12:e100839 View