This article summarizes best practices to ensure safe use of large language models in radiology, highlighting data privacy and bias.
As large language models (LLMs) and other generative artificial intelligence (AI) models are rapidly integrated into radiology workflows, unique pitfalls threatening their safe use have emerged. Problems with AI are often identified only after public release, highlighting the need for preventive measures to mitigate negative impacts and ensure safe, effective deployment into clinical settings. This article summarizes best practices for the safe use of LLMs and other generative AI models in radiology, focusing on three key areas that can lead to pitfalls if overlooked: regulatory issues, data privacy, and bias. To address these areas and minimize risk to patients, radiologists must examine all potential failure modes and ensure vendor transparency. These best practices are based on the best available evidence and the experiences of leaders in the field. Ultimately, this article provides actionable guidelines for radiologists, radiology departments, and vendors using and integrating generative AI into radiology workflows, offering a framework to prevent these problems.
No takes yet. Share an insight, caveat, or question.
Yi et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: