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Integrating Large Language Models Into Radiology Education: An Interpretation-Centric Framework for Enhanced Learning

Shawn K Lyo1, Tessa S Cook2

  • 1Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Baptist Health, Miami, Florida; Member, Society of Imaging Informatics in Medicine Members in Training Committee; American College of Radiology Informatics Advisory Council.

Journal of the American College of Radiology : JACR
|July 14, 2025
PubMed
Summary

Large language models (LLMs) can improve radiology education by offering real-time support during case interpretation and analysis. This framework enhances trainee learning and clinical efficiency.

Keywords:
Artificial intelligencelarge language modelsprecision educationradiology education

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Increasing clinical workloads in radiology limit trainee supervision and real-time feedback.
  • Current educational methods struggle to keep pace with the demands of modern radiology practice.

Purpose of the Study:

  • To present an interpretation-centric framework for integrating large language models (LLMs) into radiology education.
  • To outline how LLMs can support radiology trainees across different phases of clinical workflow.

Main Methods:

  • A phased framework (predictation preparation, active dictation, postdictation analysis) for LLM integration.
  • LLM functionalities include case summarization, differential diagnosis support, and performance analysis.

Main Results:

  • LLMs can provide context-aware case summaries and triage cases by educational value.
  • Real-time dictation support includes differential diagnosis, completeness checks, and structured follow-up.
  • Postdictation analysis identifies discrepancies, suggests improvements, and tracks trainee progress.

Conclusions:

  • This framework offers a comprehensive approach to leveraging LLMs in radiology education.
  • LLM integration has the potential to significantly enhance trainee learning while maintaining clinical efficiency.