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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.13693 |
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| _version_ | 1866912729097306112 |
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| author | Liu, Zhengliang Zhong, Tianyang Li, Yiwei Zhang, Yutong Pan, Yi Zhao, Zihao Dong, Peixin Cao, Chao Liu, Yuxiao Shu, Peng Wei, Yaonai Wu, Zihao Ma, Chong Wang, Jiaqi Wang, Sheng Zhou, Mengyue Jiang, Zuowei Li, Chunlin Holmes, Jason Xu, Shaochen Zhang, Lu Dai, Haixing Zhang, Kai Zhao, Lin Chen, Yuanhao Liu, Xu Wang, Peilong Chen, Junhao Yan, Pingkun Liu, Jun Ge, Bao Sun, Lichao Zhu, Dajiang Li, Xiang Liu, Wei Cai, Xiaoyan Hu, Xintao Jiang, Xi Zhang, Shu Zhang, Xin Zhang, Tuo Zhao, Shijie Li, Quanzheng Zhu, Hongtu Shen, Dinggang Liu, Tianming |
| author_facet | Liu, Zhengliang Zhong, Tianyang Li, Yiwei Zhang, Yutong Pan, Yi Zhao, Zihao Dong, Peixin Cao, Chao Liu, Yuxiao Shu, Peng Wei, Yaonai Wu, Zihao Ma, Chong Wang, Jiaqi Wang, Sheng Zhou, Mengyue Jiang, Zuowei Li, Chunlin Holmes, Jason Xu, Shaochen Zhang, Lu Dai, Haixing Zhang, Kai Zhao, Lin Chen, Yuanhao Liu, Xu Wang, Peilong Chen, Junhao Yan, Pingkun Liu, Jun Ge, Bao Sun, Lichao Zhu, Dajiang Li, Xiang Liu, Wei Cai, Xiaoyan Hu, Xintao Jiang, Xi Zhang, Shu Zhang, Xin Zhang, Tuo Zhao, Shijie Li, Quanzheng Zhu, Hongtu Shen, Dinggang Liu, Tianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13693 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluating Large Language Models for Radiology Natural Language Processing Liu, Zhengliang Zhong, Tianyang Li, Yiwei Zhang, Yutong Pan, Yi Zhao, Zihao Dong, Peixin Cao, Chao Liu, Yuxiao Shu, Peng Wei, Yaonai Wu, Zihao Ma, Chong Wang, Jiaqi Wang, Sheng Zhou, Mengyue Jiang, Zuowei Li, Chunlin Holmes, Jason Xu, Shaochen Zhang, Lu Dai, Haixing Zhang, Kai Zhao, Lin Chen, Yuanhao Liu, Xu Wang, Peilong Chen, Junhao Yan, Pingkun Liu, Jun Ge, Bao Sun, Lichao Zhu, Dajiang Li, Xiang Liu, Wei Cai, Xiaoyan Hu, Xintao Jiang, Xi Zhang, Shu Zhang, Xin Zhang, Tuo Zhao, Shijie Li, Quanzheng Zhu, Hongtu Shen, Dinggang Liu, Tianming Computation and Language 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. |
| title | Evaluating Large Language Models for Radiology Natural Language Processing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2307.13693 |