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大型语言模型在瘤学多学科团队会议中的实用性:系统性审查

Swetha Prabhakaran¹, Stephen Bell¹, James C Lee²

¹Department of Colorectal Surgery, Alfred Hospital, Melbourne, Victoria, Australia; School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology

|March 11, 2026

概括

大型语言模型 (LLM) 显示了癌症治疗建议的潜力,但与多学科团队的一致性不一致. 需要进一步的研究,以确保临床瘤学决策的安全性和可靠性.

科学领域:

在瘤学中使用人工智能
临床决策支持系统临床决策支持系统
医疗信息学医疗信息学

背景情况:

大型语言模型 (LLM) 是新兴的人工智能系统,具有临床决策的潜力.
传统上,瘤学决策依赖于多学科团队 (MDTs).
评估LLM在癌症治疗建议中的作用至关重要.

研究的目的:

系统地审查和评估LLM的瘤决策能力.
将LLM治疗建议与黄金标准MDT决策进行比较.
确定LLM在癌症护理中的一致率和局限性.

主要方法:

对PubMed,EMBASE和Medline数据库的系统审查 (搜索时间为1月20日).
包括同行评审的出版物,比较癌症患者的LLM和MDT建议.
排除虚构的案件,案例报告和会议记录;使用QUADAS-2进行偏见评估.

主要成果:

审查了34篇出版物,包括3513例患者病例;观察到高异质性.
在LLM和MDT建议之间,一致率差异很大 (16-100%).
当LLM遵循国际指导方针时,前列腺癌的一致性最高;三分之一的研究具有高偏差.

结论:

在瘤学决策中,LLM的表现变化不定,安全性和有效性不一致.
限制包括过度处理,缺乏可复制性,隐私问题以及偶尔的危险建议.
需要强大的前性研究来确定LLM在临床瘤学的真正实用性和安全性.

关键词:

人工智能的人工智能是人工智能.大型语言模型.多学科会议多学科会议.手术瘤学手术瘤学

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