年鉴 - 临床医学检索增强语言模型
Cyril Zakka1, Rohan Shad2, Akash Chaurasia3
1Department of Cardiothoracic Surgery, Stanford Medicine, Stanford, CA.
NEJM AI
|February 12, 2024
概括
大型语言模型 (LLM) 在医学上表现有前途,但可能会出现错误. 具有医疗数据访问权限的LLMAlmanac在临床问题答案方面表现出比标准LLM更好的准确性和安全性.
科学领域:
- 人工智能的人工智能
- 临床医学 临床医学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 对各种自然语言任务具有零射击能力.
- 尽管临床医学具有潜力,但LLM的采用受到事实上的不准确性和安全问题的阻碍.
研究的目的:
- 评估Almanac,一个LLM框架,从精心策划的医疗资源中获取,用于医疗指南和治疗建议.
- 通过使用临床问题,比较Almanac与标准LLM (ChatGPT-4,Bing,Bard) 的表现.
主要方法:
- 由八名经过董事会认证的临床医生和两名医疗保健从业人员组成的小组评估了LLM的反应.
- 该评估使用了一套新型数据集,包括9个医学专业的314个临床问题.
- 来自Almanac,ChatGPT-4,Bing和Bard的回复进行了比较.
主要成果:
- 与标准的法学士相比,Almanac在事实性和完整性方面取得了显著的改进.
- 阿尔曼纳克还显示了用户偏好和对抗安全性的增强.
- 在九个医学专业中评估了绩效.
结论:
- 访问特定领域 corpora 的 LLM 具有临床决策的潜力.
- 在临床部署之前,对LLM进行严格的测试至关重要,以解决局限性.
- 这些发现得到了国家卫生研究院,国家心脏,肺和血液研究所的支持.
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