评估基于光谱图的肺声音分类的几次射击提示,使用多式模式语言模型
Nicholas Dietrich1, David McShannon2, Mark F Rzepka3
1Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada.
PLOS digital health
|January 7, 2026
概括
大型语言模型 (LLM) 对肺声音分类有希望. 使用GPT-4o进行少量射击提示,比零射击提高了准确性,尽管临床使用需要进一步开发.
科学领域:
- 人工智能的人工智能
- 生物医学信号处理
- 呼吸系统医学 呼吸系统医学
背景情况:
- 传统的深度学习用于肺声分析需要大量的标记数据.
- 多模式大语言模型 (LLM) 提供了一个潜在的基于提示符的替代方案.
- 评估通用LLM用于呼吸声分类至关重要.
研究的目的:
- 评估GPT-4o在使用MEL光谱图的肺声分类方面的实用性.
- 为了比较这项任务的几次拍摄提示与零次拍摄提示.
- 在呼吸道声学中建立基于提示的多式推理的基线.
主要方法:
- 从ICBHI 2017数据库转换了6898个注释的呼吸循环到mel谱图.
- 采用GPT-4o的零射击和少数射击提示策略进行分类.
- 使用准确度,精度,回忆,F1得分和McNemar的测试来评估性能.
主要成果:
- 几次射击提示在准确度 (0.363比0.320) 和其他指标方面取得了统计学上显著的改善.
- 模型可重复性分析显示高度一致 (κ = 0.76-0.88),表明出色的一致性.
- 绩效增长有限,但在统计学上是显著的 (p < 0.001).
结论:
- GPT-4o显示出基于提示的肺声音分类的潜力,少数射击提示的性能优于零射击.
- 目前的性能不足以用于临床部署,但为未来的研究提供了基础.
- 基于提示的多模式推断为呼吸医学中的谱图分析提供了灵活的方法.
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