评估大型语言模型对头病史和外周静脉障碍诊断的有效性
Bonnie Lu1, Ana-Maria Misariu2, Jennifer Inseon Ham2
1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
概括
大型语言模型在诊断外围前置障碍方面表现有前途. 一个开源的LLM实现了71.4%的准确性,帮助初级保健在的诊断.
科学领域:
- 神经学 神经学
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 人工智能的人工智能
背景情况:
- 头是一种常见的初级保健症状,具有复杂的诊断.
- 诊断延迟和专家转诊在管理外周前置疾病 (PVDs) 中很常见.
- 大型语言模型 (LLM) 提供了临床决策支持的潜力.
研究的目的:
- 评估一个开源的LLM (LLaMA-3.1-8B) 用于诊断PVDs.
- 评估合成数据增强对LLM诊断准确性的影响.
- 探索用于优化LLM绩效的高级提示技术.
主要方法:
- 对100个成年PVD病例的回顾性图表审查.
- 增加了40个由GPT-4生成的合成案例.
- 使用连锁思维和多次拍摄提示对LLaMA-3.1-8B的评估.
主要成果:
- 在合并的数据集上,LLaMA-3.1-8B实现了60.7%的top-1和71.4%的top-3精度.
- 对BPPV (90%) 和Meniere病 (80.8%) 的准确度最高.
- 对症状横向性的实质性同意 (κ=0.96),对诊断的中度同意 (κ=0.41).
结论:
- 该LLM展示了对PVDs的有希望的诊断准确性.
- 合成数据增强增强了模型性能.
- LLM可以作为初级保健医生和耳鼻喉科医生有效的查工具.
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