TY - JOUR
T1 - Impact of ChatGPT and Large Language Models on Radiology Education
T2 - Association of Academic Radiology—Radiology Research Alliance Task Force White Paper
AU - Ballard, David H.
AU - Antigua-Made, Alexander
AU - Barre, Emily
AU - Edney, Elizabeth
AU - Gordon, Emile B.
AU - Kelahan, Linda
AU - Lodhi, Taha
AU - Martin, Jonathan G.
AU - Ozkan, Melis
AU - Serdynski, Kevin
AU - Spieler, Bradley
AU - Zhu, Daphne
AU - Adams, Scott J.
N1 - Publisher Copyright:
© 2024 The Association of University Radiologists
PY - 2024
Y1 - 2024
N2 - Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology—Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, and potential biases, which must be carefully considered. We provide recommendations for the successful integration of LLMs and LLM-based educational tools into radiology education programs, emphasizing assessment of the technological readiness of LLMs for specific use cases, structured planning, regular evaluation, faculty development, increased training opportunities, academic-industry collaboration, and research on best practices for employing LLMs in education.
AB - Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology—Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, and potential biases, which must be carefully considered. We provide recommendations for the successful integration of LLMs and LLM-based educational tools into radiology education programs, emphasizing assessment of the technological readiness of LLMs for specific use cases, structured planning, regular evaluation, faculty development, increased training opportunities, academic-industry collaboration, and research on best practices for employing LLMs in education.
KW - Artificial intelligence
KW - Assessment
KW - Curriculum
KW - Large language models
KW - Teaching and learning
UR - http://www.scopus.com/inward/record.url?scp=85210748225&partnerID=8YFLogxK
U2 - 10.1016/j.acra.2024.10.023
DO - 10.1016/j.acra.2024.10.023
M3 - Article
C2 - 39616097
AN - SCOPUS:85210748225
SN - 1076-6332
JO - Academic radiology
JF - Academic radiology
ER -