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Commentary: Leveraging Large Language Models for Radiology Education and Training.

Shiva Singh¹, Aditi Chaurasia¹, Surbhi Raichandani²

¹Diagnostic Radiology, University of Arkansas for Medical Sciences, Little Rock, AR.

Journal of Computer Assisted Tomography

|March 31, 2025

Summary

Large language models (LLMs) offer transformative potential in medical education, particularly in radiology training. While enhancing diagnostic skills and research, careful consideration of ethical challenges and potential biases is crucial for optimal integration.

Keywords:

artificial intelligence large language models (LLMs)medical training radiology education

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Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare
Radiology Training

Background:

Artificial intelligence (AI) is rapidly evolving, with large language models (LLMs) showing significant capabilities, including passing medical board exams.
The integration of advanced AI tools in medical education presents both opportunities and challenges.
Radiology education can benefit from AI's text processing and generation abilities.

Purpose of the Study:

To explore the integration of large language models (LLMs) in Radiology education and training.
To identify current applications, future possibilities, and associated challenges of LLMs in Radiology.
To offer insights from the Early Career Committee of the Society for Advanced Body Imaging (SABI).

Main Methods:

Exploration of LLM capabilities in text processing and generation.
Analysis of LLM performance in medical contexts, including passing board exams.
Review of applications in clinical education, diagnostic skills, structured reporting, and research.

Main Results:

LLMs can enhance Radiology clinical education through interactive training, improving diagnostic skills and structured reporting.
LLMs support research by streamlining literature reviews and automating data analysis, increasing productivity.
Integration challenges include over-reliance on AI, patient privacy concerns, and potential biases in AI-generated content.

Conclusions:

LLMs hold significant potential to transform Radiology education and training.
Addressing ethical implications and limitations is essential for the responsible optimization of LLMs in healthcare.
Mindful integration is key to leveraging LLMs effectively in the healthcare system.