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瘤学大语言模型 (LLM) 的开发和评估:一个范围审查

Namya Mehan¹, Teshan Dias Desinghe², Ashirbani Saha^3,4,5

¹Integrated Biomedical Engineering and Health Sciences, McMaster University, Hamilton, Ontario, Canada.

PLOS digital health

|August 7, 2025

概括

大型语言模型 (LLM) 在瘤学中表现有前途,但应用是多样化的,往往缺乏通用性. 未来的研究应该专注于开发专门的瘤学LLM,以解决局限性和改善癌症护理.

科学领域:

在瘤学中使用人工智能
癌症研究中的自然语言处理.
机器学习在医疗保健中的应用

背景情况:

大型语言模型 (LLM) 在文本分析和生成方面取得了重大进展.
专门为瘤学开发或评估的LLM应用程序的系统研究有限.
本综述解决了在癌症领域对LLM的全面理解的需要.

研究的目的:

探索和分类LLM在瘤学中的应用.
为了确定应用程序的性质,癌症治疗阶段,LLM类型,数据源,优化技术,评估方法和限制.
分析这些发现对瘤学研究和实践的影响.

主要方法:

在主要的科学数据库 (ACM,Embase,IEEE Xplore,Medline,Scopus,Web of Science,SPIE,Engineering Village) 中进行了图书馆员辅助的范围审查.
搜索时间为2024年1月12日,如果在2024年2月29日之前接受预印件,预印件将被考虑.
从最初的14,863个搜索中,包括了60篇文章,重点是瘤学LLM应用.

主要成果:

在各种瘤学应用中,LLM的评估越来越多,基于生成预训练变压器 (GPT) 的模型是最常见的.
治疗和诊断是癌症护理中最常见的阶段.
数据来源范围从患者记录和出版物到社交媒体,即时工程作为关键的预处理步骤.
限制包括概括性,样本大小,偏见,主观性和评估指标.

结论:

在瘤学LLM应用是异质的,利用各种数据和评估方法,并针对不同的用户.
需要专门针对瘤学而不是一般用途的LLM.
未来的研究必须解决有限的概括性,偏见缓解和评估方法的标准化,以加强临床整合.

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