Paper
Evaluating Large Language Models for Radiology Natural Language Processing
Authors
Zheng Liu, Tianyang Zhong, Yiwei Li, Yutong Zhang, Yirong Pan, Zihao Zhao, Pei Dong, Chao-Yang Cao, Yu‐Xin Liu, Peng Shu, Yaonai Wei, Zihao Wu, Chong-Yi Ma, Jiaqi Wang, Shengming Wang, Mengyue Zhou, Zuowei Jiang, Chunlin Li, Shaochen Xu, Lu Zhang, Haixing Dai, Kailiang Zhang, X. Liu, Lin Zhao, Pei Wang, Pingkun Yan, Jun Liu, Bao Ge, Lichao Sun, Dajiang Zhu, Xiang Li, W. Liu, Xiaoyan Cai, Xintao Hu, Xi Jiang, Shu Zhang, Xin Zhang, Tuo Zhang, Shijie Zhao, Quanzheng Li, Hongtu Zhu, Dinggang Shen, Tianming Liu
Abstract
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.