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Background: The integration of Artificial Intelligence (AI) into medical education is expanding, creating a need to understand its applications and implications. Objective: This umbrella review synthesizes evidence from systematic reviews to explore the integration, impact, and ethical issues of AI in medical education. Methods: A systematic review was conducted following PRISMA 2020 guidelines. PubMed (simple and MeSH) was searched for English-language studies published between January 2014 and May 2024 using the terms “artificial intelligence” and “medical education.” The methodological quality of included reviews was assessed with AMSTAR 2 and complemented by the PRISMA checklist. Results: From 196 records, 14 systematic reviews met inclusion criteria. Overall quality was moderate to low. Findings emphasized AI’s role in personalizing learning, enhancing educational tools, and supporting clinical decision-making training. Reported challenges included uneven integration, limited instructor expertise, and persistent ethical concerns. Conclusion: AI shows strong potential to transform medical education, but its adoption faces practical and ethical barriers. Early AI training for students and educators, stronger collaboration with technology experts, and higher-quality research are essential to support evidence-based and ethically sound integration.

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Artificial Intelligence in Medical Education: An Umbrella Review

  • 会议论文

  • 发表日期：

    2026年

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  • Somaia El Ghazi,
  • Nouhaila Charef,
  • Noura Qarmiche,
  • Hind Bourkhime,
  • Mohammed Omari,
  • Samira El Fakir,
  • Nada Otmani

摘要

Background: The integration of Artificial Intelligence (AI) into medical education is expanding, creating a need to understand its applications and implications. Objective: This umbrella review synthesizes evidence from systematic reviews to explore the integration, impact, and ethical issues of AI in medical education. Methods: A systematic review was conducted following PRISMA 2020 guidelines. PubMed (simple and MeSH) was searched for English-language studies published between January 2014 and May 2024 using the terms “artificial intelligence” and “medical education.” The methodological quality of included reviews was assessed with AMSTAR 2 and complemented by the PRISMA checklist. Results: From 196 records, 14 systematic reviews met inclusion criteria. Overall quality was moderate to low. Findings emphasized AI’s role in personalizing learning, enhancing educational tools, and supporting clinical decision-making training. Reported challenges included uneven integration, limited instructor expertise, and persistent ethical concerns. Conclusion: AI shows strong potential to transform medical education, but its adoption faces practical and ethical barriers. Early AI training for students and educators, stronger collaboration with technology experts, and higher-quality research are essential to support evidence-based and ethically sound integration.