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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

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