Radiologically Relevant Clinical History Summarization with Large Language Models: A Multireader Performance Study

Radiology 2026

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Authors: Adrian Serapio, Timothy L. Chen, Brian Tangsombatvisit, Brandon K.K. Fields, Yannan Yu, Yue Guo, Soo Kyung Kim, Brenda Y. Miao, Madhumita Sushil, Christopher Paul Hess, Sharmila Majumdar, Jae Ho Sohn
DOI: 10.1148/radiol.253238
Cited by: 1
References: 15

Abstract

Proprietary and open-source large language models (LLMs) generated radiologically relevant indications from clinical notes that were more comprehensive and factual than clinician indications, and when generated by the proprietary LLM, were ranked most useful in protocoling and imaging interpretation by radiologists.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Topic Modeling Machine Learning in Healthcare

Publication details

Journal / source
Radiology
Publisher
Radiological Society of North America (RSNA)
Volume
320
Issue
2
Pages
e253238–e253238
Publication date
1 August 2026
Language
EN