Fine-Tuned Large Language Model for Automated Radiology Impression Generation: A Multicenter Evaluation

Radiology Artificial Intelligence 2026 Open access

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Authors: Mingyang Li, Yaning Wang, Miao Zheng, Jiaqi Gong, Simin Yang, Han Xue, Qi Yang, Lijun Duan, Lin Mu, Ying Mu, Kai Zhu, Qi Dai
+8 more Munire Aihemaiti, Yunhui Yang, Lu Liu, Yingyan Zheng, Yang Hou, Lei Zhang, Jing Wang, Huimao Zhang
DOI: 10.1148/ryai.250714
Cited by: 1
References: 29

Abstract

Large-scale fine-tuning produced a highly accurate large language model for automated radiology impression generation, demonstrating a validated approach to improve reporting efficiency and consistency across multicenter examinations.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Radiology practices and education Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Radiology Artificial Intelligence
Publisher
Radiological Society of North America (RSNA)
Volume
8
Issue
3
Pages
e250714–e250714
Publication date
15 April 2026
Language
EN