Large Language Model–Generated Expansion of the RadLex Ontology: Application to Multinational Datasets of Chest CT Reports

American Journal of Roentgenology 2026 Open access

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Authors: Taehee Lee, Hyungjin Kim, Seowoo Lee, Seonhye Chae, Charles E. Kahn, Seng Chan You, Edward Jongyoon Choi, Soon Ho Yoon
DOI: 10.2214/ajr.25.34243
Cited by: 1
References: 16

Abstract

The LLM-based approach provides a practical and scalable solution for expanding radiology ontologies while maintaining semantic alignment; the method can aid real-world natural language processing applications.

Fields of Study & Subjects

Biomedical Text Mining and Ontologies Artificial Intelligence in Healthcare and Education Radiology practices and education

Publication details

Journal / source
American Journal of Roentgenology
Publisher
American Roentgen Ray Society
Volume
226
Issue
5
Pages
e2534243–e2534243
Publication date
14 January 2026
Language
EN