Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.

ABSTRACT:

Background

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.

Methods

In this study, we introduce a framework to intelligently leverage language

SUBMITTER: Yao MS 

PROVIDER: S-EPMC12322208 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature