Best Practices for Large Language Models in Radiology

Abstract

Radiologists must integrate complex imaging data with clinical information to produce actionable insights. This task requires a nuanced application of language across many activities, including managing clinical requests, analyzing imaging findings in the context of clinical data, interpreting these through the radiologist's lens, and effectively documenting and communicating the outcomes. Radiology practices must ensure reliable communication among numerous systems and stakeholders critical for medical decision-making. Large language models (LLMs) offer an opportunity to improve the management and interpretation of the vast amounts of text data in radiology. Despite being developed as general-purpose tools, these advanced computational models demonstrate impressive capabilities in specialized tasks, even without specific training. Unlocking the potential of LLMs for radiology requires an understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise, provides general and technically adept radiologists insight into the potential of LLMs in radiology. It also equips those interested in implementing applicable best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. The review provides practical advice for optimizing LLM characteristics for radiology practices, including advice on limitations, effective prompting, and fine-tuning strategies.

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