TY - JOUR
T1 - The Advent of Generative Language Models in Medical Education
AU - Karabacak, Mert
AU - Ozkara, Burak Berksu
AU - Margetis, Konstantinos
AU - Wintermark, Max
AU - Bisdas, Sotirios
N1 - Publisher Copyright:
© 2023 Authors. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - academic integrity
KW - AI-driven feedback
KW - artificial intelligence
KW - ChatGPT
KW - evaluation
KW - generative language model
KW - learning environment
KW - medical education
KW - medical student
KW - stimulation
KW - technology
UR - http://www.scopus.com/inward/record.url?scp=85164319374&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164319374&partnerID=8YFLogxK
U2 - 10.2196/48163
DO - 10.2196/48163
M3 - Article
C2 - 37279048
AN - SCOPUS:85164319374
SN - 2369-3762
VL - 9
JO - JMIR Medical Education
JF - JMIR Medical Education
M1 - e48163
ER -