Back to search

Article

Large Language Models for Classifying Usual Interstitial Pneumonia from Radiology Reports: Native Reasoning Versus Structured Prompting

2026-04-20

Abstract excerpt

<title>Abstract</title> <p>Extracting disease labels from radiology reports is essential for developing deep learning-based diagnostic models and enabling large-scale retrospective clinical research. Classification of usual interstitial pneumonia (UIP) patterns from high-resolution computed tomography (HRCT) reports according to Fleischner Society guidelines is a particularly demanding task, requiring synthesis o...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
95dd07cd-effd-507c-b932-325b278716fc
DOI
10.21203/rs.3.rs-9442328/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Large Language Models for Classifying Usual Interstitial Pneumonia from Radiology Reports: Native Reasoning Versus Structured PromptingDOI 10.21203/rs.3.rs-9442328/v1
Select a neighboring publication to make it the new centre.