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Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

Alaa Abd-Alrazaq1, Rawan AlSaad1,2, Dari Alhuwail3

  • 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

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Summary

Large language models (LLMs) offer transformative potential for medical education, enhancing learning and curricula. However, careful consideration of ethical challenges like bias and misinformation is crucial for responsible AI integration.

Keywords:
ChatGPTGPT-4artificial intelligenceeducatorsgenerative AIlarge language modelsmedical educationstudents

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The landscape of medical education is shifting towards AI-driven paradigms.
  • Large Language Models (LLMs) present novel opportunities for enhancing medical training.

Purpose of the Study:

  • To explore the potential benefits of integrating LLMs into medical education.
  • To critically analyze the challenges and ethical considerations associated with LLM implementation in medical training.

Main Methods:

  • Review of current literature and expert opinion on LLM applications in education.
  • Analysis of potential impacts on curriculum development, teaching, and assessment.
  • Examination of ethical concerns including bias, plagiarism, and data privacy.

Main Results:

  • LLMs can revolutionize curriculum design, personalized learning, and student assessment.
  • Significant challenges exist, including algorithmic bias, misinformation, and copyright issues.
  • Responsible integration requires addressing equity, privacy, and overreliance concerns.

Conclusions:

  • LLMs hold significant promise for advancing medical education.
  • Addressing ethical and practical challenges is paramount for effective and equitable AI adoption.
  • This analysis provides a foundation for developing best practices in AI-driven medical education.