对于分子瘤板的检索增强疗法建议:算法开发和验证研究
Eliza Berman1, Holly Sundberg Malek2, Michael Bitzer3
1Center for Digital Health, University Hospital Tuebingen, Tuebingen, Germany.
Journal of medical Internet research
|March 7, 2025
概括
大型语言模型 (LLM) 可以通过生成治疗建议来协助分子瘤板 (MTB). 虽然LLM在精确瘤学中表现有前途,但由于偶尔出现不准确性和幻觉,临床医生监督是必不可少的.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 分子瘤板 (MTBs) 涉及对患者治疗建议进行广泛的手册审查.
- 大型语言模型 (LLM) 有潜力提高MTB的效率,减少错误,改善精密瘤学的护理可访问性.
研究的目的:
- 评估LLM为MTB患者生成的治疗建议的有效性.
- 评估LLM在提供基于证据的推与PubMed引用方面的能力.
主要方法:
- 使用PubMed数据开发了一个检索增强生成管道.
- 要求LLM为试验患者队列生成与PubMed参考的治疗建议.
- MTB成员手动审查了LLM生成的建议的准确性和相关性.
主要成果:
- 75%的引用文章正确地来源于PubMed; 17%被确定为幻觉.
- 临床医生评估的LLM查询显示了比自动化查询更高的准确性.
- 临床医生发现25%的LLM反应相当于他们自己的,而37.5%是可信的替代方案.
结论:
- 通过LLM增强的检索增强生成可以加速MTB过程,有时会产生与临床医生相当的建议.
- 需要进一步的研究来消除幻觉,并确保一致的LLM表现.
- LLM为MTB调查提供了可扩展的解决方案,但需要大量的临床医生监督,并且无法完全自动化该过程.
关键词:
在法律上,LLMs.拉拉玛拉拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛最多可能的TBT.可以获得护理的机会.增强疗法是一种增强疗法.临床试验是指临床试验中的临床试验.基于证据的基于证据的证据.大型语言模型.分子瘤分子瘤.分子瘤板是一个分子瘤板.瘤学 在瘤学方面.患者护理 患者护理精确瘤学 精确瘤学提取增强代的恢复治疗治疗治疗治疗治疗治疗瘤是一个瘤.更多相关视频
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