评估实验室测试结果对大型语言模型的影响,从临床病例视频中产生了差异诊断
Balu Bhasuran1, Qiao Jin2, Yuzhang Xie3
1Florida State University, Tallahassee, FL.
ArXiv
|October 1, 2025
概括
实验室测试结果显著提高了大型语言模型 (LLM) 在生成差异诊断 (DDx) 的准确性,GPT-4在区分类似的医疗条件方面表现最好.
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:
- 差异诊断 (DDx) 对于准确的医学评估至关重要.
- 大型语言模型 (LLM) 显示出有助于临床决策的潜力.
- 实验室数据对LLM生成的DDx的影响需要系统评估.
研究的目的:
- 评估实验室测试结果对LLMs产生的差异诊断的影响.
- 为了比较各种LLM在产生准确的DDx与没有实验室数据的性能.
- 评估在DDx.的背景下对常见实验室试验的LLM的解释性准确性.
主要方法:
- 从PubMed Central病例报告创建50个临床图片,包括患者人口统计,症状和实验室结果.
- 测试五种LLM (GPT-4,GPT-3.5,Llama-2-70b,Claude-2,Mixtral-8×7B) 的Top1,Top5和Top10DDx生成,带有和没有实验室数据.
- 综合评估使用GPT-4,知识图和临床医生的输入.
主要成果:
- 当包括实验室数据时,GPT-4在Top 1 DDx中实现了55%的准确率,在Top 10 DDx中达到60%,轻度准确率达到80%.
- 包括实验室结果显著提高了LLM的性能,特别是对于GPT-4和Mixtral.
- 在解释肝功能,代谢/毒理学面板和DDx.的血清学/免疫测试等关键实验室测试方面,LLMs表现出了一般准确性.
结论:
- 实验室测试结果对于提高LLM辅助差异诊断的准确性至关重要.
- 虽然DDx的确切匹配率仍然很低,但LLM作为支持临床推理的工具具有前景.
- 需要进一步的研究,以优化LLM整合到差异诊断的临床工作流程中.
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