通过与放射学指导方针的语言模型对齐,评估真实世界患者病例的急性图像排序
Michael S Yao1,2, Allison Chae2, Piya Saraiya3
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Communications medicine
|August 4, 2025
概括
生成型人工智能和大型语言模型现在可以推诊断成像研究,匹配临床医生的准确性. 这种人工智能工具有助于急诊室,改善对基于证据的指导方针的遵守,以获得更好的患者护理.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 医疗成像医学成像
背景情况:
- 诊断成像对于急性护理至关重要,但订购是可变的.
- 生成型人工智能和大型语言模型 (LLM) 显示出对符合指导方针的成像建议有前景.
- 在确保AI建议与有限的急性护理诊断信息保持一致方面仍然存在挑战.
研究的目的:
- 引入一个框架,利用LLM来推与美国放射学学院的适当性标准一致的成像研究.
- 为了使最先进的LLM能够对患者病例进行适当的成像选择进行推理.
主要方法:
- 开发了一个框架,以智能地利用语言模型来成像研究建议.
- 创建了RadCases,一个包含1500多个注释案例摘要的数据集.
- 使用RadCases数据集将框架应用于LLM.
主要成果:
- 在使用该框架的成像研究中,LLMs的准确性与临床医生相美.
- 该LLM管道的功能作为临床医生的智能助理.
- 提高了急性图像排序的准确性,与ACR适当性标准保持一致.
结论:
- 验证了人工智能软件的战略,以加强可信的临床决策.
- 人工智能驱动的建议可以提高对专家基于证据的指导方针的遵守.
- 该框架支持在急性护理环境中改进诊断成像实践.
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