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Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study.

Majdi QuttainahVinaytosh MishraSomayya MadakamYotam LurieShlomo Mark
Published in: JMIR AI (2024)
This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.
Keyphrases
  • medical education
  • healthcare
  • public health
  • health information
  • autism spectrum disorder
  • transcription factor
  • health promotion