2025 Expert consensus on retrospective evaluation of large language model applications in clinical scenarios

Large language models (LLMs), trained on vast amounts of textual data, have demonstrated strong capabilities in natural language understanding and generation. In the medical field, LLMs are increasingly applied across various domains such as disease screening, diagnostic assistance, and health manag...

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Dettagli Bibliografici

Autori principali:	Qing Chang, Fei Chen, Yaolong Chen, Longlong Cheng, Di Dong, Jiahong Dong, Xiaobin Feng, Junbo Ge, Jingjing He, Yihua He, Zhiyang He, Hong Ji, Xue Jiang, Zehua Jiang, Nan Li, Peng Li, Yazi Li, Bing Liu, Junwei Liu, Han Lyu, Dong Min, Wen'an Qi, Xibin Shen, Bin Sheng, Jing Sun, Yujie Sun, Binglei Tian, Kailei Wang, Li Wang, Lihua Wang, Weimin Wang, Ye Wang, Yuqin Wang, Zhenchang Wang, Jianping Weng, Junmin Wei, Gang Wu, Xiaojian Wu, Yue Xiao, Yanwu Xu, Peng Yan, Zhewei Ye, Wanhong Yin, Chengwen Zhang, Di Zhang, Pengjun Zhang, Wensheng Zhang, Xudong Zhang, Shijie Zhao, Yi Zhao, Shaohua Zhou, Xiang Zhou, Baoliang Zhu, Lifeng Zhu, Zuyi Zhu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Elsevier 2025-11-01
Serie:	Intelligent Medicine
Soggetti:	Large language models Clinical scenarios Retrospective evaluation Expert consensus
Accesso online:	http://www.sciencedirect.com/science/article/pii/S2667102625001044
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