Paul H Yi, Hana L Haver, Jean J Jeudy, Woojin Kim, Felipe C Kitamura, Eniola T Oluyemi, Andrew D Smith, Linda Moy, Vishwa S Parekh
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Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.
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.
期刊介绍:
Published regularly since 1923 by the Radiological Society of North America (RSNA), Radiology has long been recognized as the authoritative reference for the most current, clinically relevant and highest quality research in the field of radiology. Each month the journal publishes approximately 240 pages of peer-reviewed original research, authoritative reviews, well-balanced commentary on significant articles, and expert opinion on new techniques and technologies.
Radiology publishes cutting edge and impactful imaging research articles in radiology and medical imaging in order to help improve human health.