广泛训练有素的LLM在解读骨科Walch腺分类中的局限性
Adam ElSayed1, Gary F Updegrove1
1Penn State Health Milton S. Hershey Medical Center, Hershey, PA, United States.
Frontiers in artificial intelligence
|September 15, 2025
概括
经过广泛训练的大型语言模型 (LLM) 在解释简化医疗图表时显示出有限的准确性,比如肩膀手术的状腺分类. 专门的AI培训对于临床环境中可靠的医学图像分析至关重要.
科学领域:
- 医疗人工智能 医疗人工智能
- 计算机视觉在医疗保健中的应用
- 整形外科 影像成像 整形外科
背景情况:
- 人工智能 (AI) 在医学中的采用正在迅速增加,在2023年至2024年期间,医生的使用量几乎翻了一番.
- 大型语言模型 (LLM) 正在发展,结合多式联络功能,有可能用于医学图像解释和临床工作流集成.
研究的目的:
- 评估两个著名的LLM的准确性,克劳德3.5索内特和DeepSeek R1,在解释沃尔奇的状腺分类图.
- 通过简化医学图表,评估LLM在手术前肩部重建规划中的表现.
主要方法:
- 来自Radiopedia的七个黑白Walch状腺图被用作测试图像.
- 通过Perplexity.ai访问LLM,并在没有专门的医疗培训的情况下使用不同的提示长度 (22-864字) 进行测试.
- 性能通过根据沃尔奇分类系统对状腺图的分类准确度来衡量.
主要成果:
- 观察到显著的性能差异:DeepSeek获得了44%的准确性 (7/16正确),而Claude获得了0%的准确性 (0/16正确).
- DeepSeek 在指令长度和准确性之间显示出轻微的正相关性.
- 常见的错误包括两种模型错误地将A2状腺类型分类为A1 (32%) 或B2 (20%).
结论:
- 经过广泛培训的LLM目前缺乏可靠的医学图像解释所需的准确性和一致性,即使使用简化图表.
- 由于DeepSeek的持续学习和开源数据,DeepSeek的卓越性能仍然不足以用于临床使用.
- 限制源于LLM培训数据主要以文本为特色,导致医学成像视觉模式识别的缺陷;专业培训对于临床实施至关重要.
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