使用大型语言模型对囊的波斯尼亚语分类:一项比较研究
1Department of Urology, Basaksehir Çam and Sakura City Hospital, Istanbul, Turkey.
Radiologie (Heidelberg, Germany)
|August 24, 2025
概括
大型语言模型 (LLM) 可以从文本描述中准确地分类囊,少量学习显著提高了性能. GPT-4显示了最高的准确性,尽管分类具有挑战性的波斯尼亚IIF病变仍然是一个需要发展的领域.
科学领域:
- 放射学中的人工智能
- 医学成像分析
- 在医疗保健中的自然语言处理
背景情况:
- 博斯尼亚克分类系统有助于评估囊恶性瘤风险.
- 波斯尼亚语分类的观察者间变异性是一个诊断挑战.
- 大型语言模型 (LLM) 提供了使用文本数据进行标准化瘤分类的潜力.
研究的目的:
- 根据合成CT报告描述,评估五种LLM在分类囊中的性能.
- 基于LLM的囊分类的零射击和少数射击提示策略进行比较.
- 评估LLM在区分波斯尼亚人的准确性,敏感性和特异性.
主要方法:
- 创建了一个100例囊病例的合成数据集 (每个波斯尼亚人类别为20例).
- 测试了五种LLM (GPT-4,Gemini,Copilot,Perplexity和NotebookLM) 的使用情况.
- 使用零射击,少射击和提取增强生成 (RAG) 提示策略.
主要成果:
- GPT-4获得了最高的准确性 (99%的几次学习).
- 在不同模型中显著提高了LLM性能 (p < 0.05).
- 对于所有评估的LLM来说,波斯尼亚IIF病变是具有挑战性的.
结论:
- 当提供结构化的文字描述时,LLM可以准确地分类囊.
- 不少射击学习大大提高了囊的LLM分类准确性.
- 需要进一步的研究来解决边界玻斯尼亚克IIF病变的分类问题.
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