专用人工智能专家系统与具有临床诊断的大型语言模型的生成人工智能
Mitchell J Feldman1, Edward P Hoffer1, Jared J Conley1
1Laboratory of Computer Science, Massachusetts General Hospital, Boston.
JAMA network open
|May 29, 2025
概括
大型语言模型 (LLM) 和诊断决策支持系统 (DDSS) 在诊断未发表的临床病例方面表现相似. 包括实验室结果在内显著提高了所有系统的诊断准确度,这表明了混合方法的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 已经成为强大的工具,但与传统系统相比,它们的诊断能力尚未成熟.
- 诊断决策支持系统 (DDSS) 是成熟的工具,但它们与新临床数据上的新型LLM相比的性能需要评估.
研究的目的:
- 将两个领先的LLM (ChatGPT-4和Gemini 1.5) 与传统的DDSS (DXplain) 的诊断性能进行比较.
- 用未发表的临床病例来评估系统性能,包括或不包括实验室测试结果.
主要方法:
- 一项涉及36个未发表的全科医生病例的诊断研究,来自三个学术医疗中心.
- 医生审核人员确定了相关的临床发现;对LLM和DDSS进行盲目数据输入,并没有实验室数据.
- 绩效由每个系统生成的前25个差异诊断中的正确诊断的存在和排名来评估.
主要成果:
- 在没有实验室数据的情况下,DDSS显示出比LLM1 (42%) 和LLM2 (39%) 更高的诊断准确度 (56%),尽管在统计学上并不显著.
- 包括实验室测试结果改善了所有系统的诊断准确性,DDSS达到72%,LLM1 64%,LLM2 58%.
- 在提供实验室数据时,所有系统都成功地将正确的诊断列为前25名,在大多数情况下.
结论:
- 当前的LLM和传统的DDSS在实验室数据被遗漏的情况下,在未发表的临床病例上表现相似,尽管在统计学上并不显著.
- 实验室测试结果的包含大大提高了LLM和DDSS的诊断准确度.
- 将LLM的语言优势与DDSS的决定性能力相结合的混合方法可能为临床决策支持提供协同效益.
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