Impact of ChatGPT and Large Language Models on Radiology Education: Association of Academic Radiology—Radiology Research Alliance Task Force White Paper

  • David H. Ballard
  • , Alexander Antigua-Made
  • , Emily Barre
  • , Elizabeth Edney
  • , Emile B. Gordon
  • , Linda Kelahan
  • , Taha Lodhi
  • , Jonathan G. Martin
  • , Melis Ozkan
  • , Kevin Serdynski
  • , Bradley Spieler
  • , Daphne Zhu
  • , Scott J. Adams

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)3039-3049
Number of pages11
JournalAcademic radiology
Volume32
Issue number5
DOIs
StatePublished - May 2025

Keywords

  • Artificial intelligence
  • Assessment
  • Curriculum
  • Large language models
  • Teaching and learning

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