在多学科乳腺癌团队中为放射科医生提供支持的大型语言模型的评估:比较研究
Hong Jiang1,2, Chun Yang3, Wenbin Zhou4
1Faculty of Medicine, Macau University of Science and Technology, Macao, China.
JMIR medical informatics
|February 2, 2026
概括
大型语言模型 (LLM) 在乳腺癌诊断方面表现有前途,一些模型在特定领域的表现优于医生. 然而,LLMs不能完全取代医生在复杂的乳腺癌病例中的临床经验.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 人工智能 (AI) 工具,特别是大型语言模型 (LLM),在各个领域都显示出巨大潜力.
- 在乳腺癌诊断和治疗中,LLMs的有效性在很大程度上仍未被探索.
- 本研究调查了LLMs在支持乳腺癌多学科团队内的放射科医生的作用.
研究的目的:
- 评估各种LLM在协助放射科医生诊断和治疗乳腺癌方面的表现.
- 评估LLM在促进知情临床决策和改善乳腺癌管理患者护理方面的能力.
- 为了比较LLM的表现与不同经验水平的放射科医生.
主要方法:
- 根据乳腺癌放射学和临床指导方针制定了一套50个问题.
- 给9名受欢迎的LLM (包括ChatGPT-4.0,Claude 3 Opus,Gemini 1.5 Pro) 和临床医生 (居民,研究员,主治医生) 进行了这些问题.
- 根据准确性,信心和一致性对既定指南 (NCCN乳腺癌指南,ACR乳腺成像报告和数据系统) 的评估反应.
主要成果:
- 克劳德3Opus和ChatGPT-4表现出高的信心分数;ChatGPT-4o在准确度方面领先.
- 克劳德3 Opus,克劳德3.5 Sonnet,聊天GPT-4o,双子座1.5 Pro和聊天GPT-4o mini显示出优越的响应一致性.
- 在总体表现方面,ChatGPT-4o mini和Claude模型的表现优于医生组,尽管统计学意义有所不同;一些LLM表现与医生相当.
结论:
- 像ChatGPT-4o和Claude 3 Opus这样的LLM显示出在多学科团队内支持乳腺癌诊断和治疗的潜力.
- 目前,LLM无法完全复制从广泛的临床经验中获得的细微决策能力,特别是在复杂的病例中.
- 持续开发人工智能对于提高临床适用性和确保可靠整合到乳腺癌护理途径至关重要.
相关概念视频
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The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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