通过与放射学指南对齐的语言模型评估真实世界患者病例的急性影像检查申请
Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.
摘要(中文译文)
诊断影像学检查在急性就诊患者的管理中日益重要。然而，在急诊科开具适当的影像学检查是一项具有挑战性的任务，且不同医疗提供者之间存在高度差异。为解决这一问题，近期研究探讨了是否可以利用生成式人工智能（generative AI）和大语言模型（large language models）依据循证医学指南推荐诊断影像学检查。然而，确保这些工具能够提供与医学指南正确一致的推荐仍具挑战性，尤其是在急性诊疗环境中可用的诊断信息有限的情况下。在本研究中，我们引入了一个框架，通过为患者病例推荐与美国放射学会（American College of Radiology）适宜性标准（Appropriateness Criteria）——一套循证指南——相一致的影像学检查，来智能地利用语言模型。为支持我们的实验，我们引入了RadCases，一个包含超过1500份标注病例摘要的数据集，反映了常见的患者就诊情况，并应用我们的框架使最先进的语言模型能够推理适当的影像学选择。使用我们的框架，最先进的语言模型在开具影像学检查方面达到了与临床医生相当的准确率。此外，我们证明，我们基于语言模型的流程可作为临床医生的智能助手，支持影像开具工作流程，并根据美国放射学会适宜性标准提高急性影像开具的准确性。我们的工作展示并验证了一种策略，即利用基于人工智能的软件来改进可信的临床决策，使其与专家循证指南保持一致。
Abstract
Diagnostic imaging studies are increasingly important in the management of acutely presenting patients. However, ordering appropriate imaging studies in the emergency department is a challenging task with a high degree of variability among healthcare providers. To address this issue, recent work has investigated whether generative AI and large language models can be leveraged to recommend diagnostic imaging studies in accordance with evidence-based medical guidelines. However, it remains challenging to ensure that these tools can provide recommendations that correctly align with medical guidelines, especially given the limited diagnostic information available in acute care settings. In this study, we introduce a framework to intelligently leverage language models by recommending imaging studies for patient cases that align with the American College of Radiology's Appropriateness Criteria, a set of evidence-based guidelines. To power our experiments, we introduce RadCases, a dataset of over 1500 annotated case summaries reflecting common patient presentations, and apply our framework to enable state-of-the-art language models to reason about appropriate imaging choices. Using our framework, state-of-the-art language models achieve accuracy comparable to clinicians in ordering imaging studies. Furthermore, we demonstrate that our language model-based pipeline can be used as an intelligent assistant by clinicians to support image ordering workflows and improve the accuracy of acute image ordering according to the American College of Radiology's Appropriateness Criteria. Our work demonstrates and validates a strategy to leverage AI-based software to improve trustworthy clinical decision-making in alignment with expert evidence-based guidelines.
如何引用
AMA
Michael S Yao, Allison Chae, Piya Saraiya, Charles E Kahn, Walter R Witschey, James C Gee, et al. Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.. Communications medicine. 2025; doi:10.1038/s43856-025-01061-9.
APA
Michael S Yao, Allison Chae, Piya Saraiya, Charles E Kahn, Walter R Witschey, James C Gee, et al (2025). Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.. Communications medicine. https://doi.org/10.1038/s43856-025-01061-9
GB/T 7714
Michael S Yao, Allison Chae, Piya Saraiya, Charles E Kahn, Walter R Witschey, James C Gee, et al. Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.[J]. Communications medicine, 2025 doi:10.1038/s43856-025-01061-9.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出