Commentary: Leveraging Large Language Models for Radiology Education and Training

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.