A systematic multimodal assessment of AI machine translation tools for enhancing access to critical care education internationally.

Journal: BMC medical education
Published Date: Jul 8, 2025

Abstract

BACKGROUND: Language barriers pose a significant barrier to expanding access to critical care education worldwide. Machine translation (MT) offers significant promise to increase accessibility to critical care content, and has rapidly evolved using newer artificial intelligence frameworks and large language models. The best approach to systematically apply and evaluate these tools, however, remains unclear.

Authors

  • Christine L Chen
    Division of Internal Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Chen.Christine@mayo.edu.
  • Yue Dong
    Department of Anesthesiology, Mayo Clinic College of Medicine, Rochester, MN, United States.
  • Claudia Castillo-Zambrano
    Division of Internal Medicine, Dartmouth Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
  • Hassan Bencheqroun
    Division of Pulmonary and Critical Care Medicine, University of California Riverside School of Medicine, 92521 Botanic Gardens Dr, Riverside, CA, 92507, USA.
  • Amelia Barwise
    Division of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
  • Adria Hoffman
    School of Continuous Professional Development, Mayo Clinic, 200 First St. SW, Rochesster, MN, 55905, USA.
  • Keivan Nalaie
    Division of Anesthesiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
  • Yishu Qiu
    Des Moines University Medicine and Health Sciences, 8025 Grand Avenue in West Des Moines, Iowa, 50266, USA.
  • Oualid Boulekbache
    Department of Language Services, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
  • Alexander S Niven
    Division of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.

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