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大语言模型推动精准肿瘤学发展的潜力。

The potential of large language models to advance precision oncology.

作者信息

Liang Shufan, Zhang Jiangjiang, Liu Xingting, Huang Yinkui, Shao Jun, Liu Xiaohong, Li Weimin, Wang Guangyu, Wang Chengdi

机构信息

Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Targeted Tracer Research and Development Laboratory, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.

出版信息

EBioMedicine. 2025 May;115:105695. doi: 10.1016/j.ebiom.2025.105695. Epub 2025 Apr 29.

Abstract

With the rapid development of artificial intelligence (AI) within medicine, the emergence of large language models (LLMs) has gradually reached the forefront of clinical research. In oncology, by mining the underlying connection between a text or image input and the desired output, LLMs demonstrate great potential for managing tumours. In this review, we provide a brief description of the development of LLMs, followed by model construction strategies and general medical functions. We then elaborate on the role of LLMs in cancer screening and diagnosis, metastasis identification, tumour staging, treatment recommendation, and documentation processing tasks by decoding various types of clinical data. Moreover, the current barriers faced by LLMs, such as hallucinations, ethical problems, limited application, and so on, are outlined along with corresponding solutions, where the further purpose is to inspire improvement and innovation in this field with respect to harnessing LLMs for advancing precision oncology.

摘要

随着人工智能(AI)在医学领域的迅速发展,大语言模型(LLMs)的出现已逐渐走到临床研究的前沿。在肿瘤学中,通过挖掘输入的文本或图像与期望输出之间的潜在联系,大语言模型在肿瘤管理方面展现出巨大潜力。在本综述中,我们简要描述了大语言模型的发展,接着介绍了模型构建策略和一般医学功能。然后,我们通过解码各类临床数据,详细阐述了大语言模型在癌症筛查与诊断、转移识别、肿瘤分期、治疗推荐以及文档处理任务中的作用。此外,还概述了大语言模型当前面临的障碍,如幻觉、伦理问题、应用受限等,并提出了相应的解决方案,其进一步目的是激发该领域在利用大语言模型推进精准肿瘤学方面的改进与创新。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0230/12083916/26dbff8753e0/gr1.jpg

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