Commentary: Leveraging Large Language Models for Radiology Education and Training

Abstract

In the rapidly evolving landscape of medical education, artificial intelligence (AI) holds transformative potential. This manuscript explores the integration of large language models (LLMs) in Radiology education and training. These advanced AI tools, trained on vast data sets, excel in processing and generating human-like text, and have even demonstrated the ability to pass medical board exams. In radiology, LLMs enhance clinical education by providing interactive training environments that improve diagnostic skills and structured reporting. They also support research by streamlining literature reviews and automating data analysis, thus boosting productivity. However, their integration raises significant challenges, including the risk of over-reliance on AI, ethical concerns related to patient privacy, and potential biases in AI-generated content. This commentary from the Early Career Committee of the Society for Advanced Body Imaging (SABI) offers insights into the current applications and future possibilities of LLMs in Radiology education while being mindful of their limitations and ethical implications to optimize their use in the health care system.

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