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05/01/26

Error detection in emergency radiology reports using a large language model: multistage evaluation study.

Shen, Hui; Wu, Tianyang; Wang, Fei; Fang, Jin; Li, Yuange; Wu, Xiaoling; Liu, Shuyi; Chen, Liting; Ren, Qiuping; Meng, Xiao; Xu, Jiatong; Sun, Jie; Zhao, Yujie; Liu, Xin; Wang, Liaoyuan; Mai, Guipeng; You, Jingjing; Jin, Zhe; Wu, Xuewei; He, Wenle; Han, Xue; Zhang, Shuixing; Zeng, Dong; Zhang, Bin. J Med Internet Res.

Collection Topics
Communication Culture, Work System, and Implementation Tests, Screening, and Laboratory Processes
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Summary

Emergency radiology demands high accuracy under time pressure, and rising workloads increase error risk. This Chinese study compared five LLMs with 12 board-certified radiologists for detecting errors in Chinese emergency radiology reports. DeepSeek-R1 showed the most promise, with performance and speed supporting use as a real-world proofreading aid.
Read the paper
Shen H, Wu T, Wang F, et al. Error detection in emergency radiology reports using a large language model: multistage evaluation study. J Med Internet Res. 2026;28:e86841.