使用临床决策支持和大型语言模型在大型欧洲队列中的CT转诊理由的比较
Mor Saban1,2, Yaniv Alon3, Osnat Luxenburg4
1School of Health Sciences, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel. Morsaban1@tauex.tau.ac.il.
European radiology
|April 27, 2025
概括
独立专家最准确地证明了CT扫描的合理性,尽管大型语言模型 (LLM) 显示了器官预测的前景. 结合人与人工智能的方法可以提高CT扫描的适当性和资源使用.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 计算机断层扫描 (CT) 扫描的适当使用对于患者安全和有效的资源分配至关重要.
- 人工智能 (AI),包括大语言模型 (LLM),为改进CT转介理由提供了潜在的潜力.
- 需要对人工智能工具进行严格的评估,并将其与专家评估和既定标准进行对比.
研究的目的:
- 为了比较LLM (GPT-4和Claude-3海库) 和人类专家在证明CT推方面的表现.
- 将这些表现与ESR iGuide临床决策支持系统作为参考标准进行评估.
主要方法:
- 对6356个CT转案案件的回顾性分析.
- 通过ESR iGuide,LLM和独立专家提出建议.
- 对测试,器官和对比度预测的准确性,精度,回忆,F1分数和科恩的卡帕的评估.
主要成果:
- 独立专家在医学测试证明方面取得了最高的准确性 (92.4%).
- 在器官预测方面,LLM的准确性与专家相似 (75.3-77.8%与82.6%对比).
- 在对比度预测方面,GPT-4显示了最高的准确性 (57.4%),而Claude-3 Haiku与指导方针的一致性非常低 (kappa = 0.006).
结论:
- 独立专家仍然是最可靠的CT转诊理由.
- LLM显示了优化潜力,特别是在器官预测方面.
- 混合人-人工智能方法可以提高CT转诊的适当性和临床决策.
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