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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