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MINI REVIEW article

Front. Med.
Sec. Healthcare Professions Education
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1525604
This article is part of the Research Topic Innovations in Teaching and Learning for Health Professions Educators View all 8 articles

Generative Artificial Intelligence in Graduate Medical Education

Provisionally accepted
Ravi Janumpally Ravi Janumpally Suparna Nanua Suparna Nanua Andy Ngo Andy Ngo Kenneth Youens Kenneth Youens *
  • Baylor Scott & White Health Clinical Informatics Fellowship Program, Round Rock, United States

The final, formatted version of the article will be published soon.

    Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potential opportunities and risks of GenAI in graduate medical education (GME). We review the existing literature and provide commentary on how GenAI could impact GME, including five key areas of opportunity: electronic health record (EHR) workload reduction, clinical simulation, individualized education, research and analytics support, and clinical decision support. We then discuss significant risks, including inaccuracy and overreliance on AI-generated content, challenges to authenticity and academic integrity, potential biases in AI outputs, and privacy concerns. As GenAI technology matures, it will likely come to have an important role in the future of GME, but its integration should be guided by a thorough understanding of both its benefits and limitations.

    Keywords: Generative AI, LLM, gpt, GME, Graduate medical education, ChatGPT, artificial intelligence, Education

    Received: 10 Nov 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Janumpally, Nanua, Ngo and Youens. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kenneth Youens, Baylor Scott & White Health Clinical Informatics Fellowship Program, Round Rock, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.