The Advent of Generative Language Models in Medical Education

Mert Karabacak, Burak Berksu Ozkara, Konstantinos Margetis, Max Wintermark, Sotirios Bisdas

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Artificial intelligence (AI) and generative language models (GLMs) present significant opportunities for enhancing medical education, including the provision of realistic simulations, digital patients, personalized feedback, evaluation methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning environments and enhance medical students' educational outcomes. However, ensuring content quality, addressing biases, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop guidelines and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care.

Original languageEnglish
Article numbere48163
JournalJMIR Medical Education
Volume9
DOIs
StatePublished - 2023
Externally publishedYes

Keywords

  • AI-driven feedback
  • ChatGPT
  • academic integrity
  • artificial intelligence
  • evaluation
  • generative language model
  • learning environment
  • medical education
  • medical student
  • stimulation
  • technology

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