提高诊断精度:使用大型语言模型从甲状腺超声波报告中提取U分数
Emma Watts1,2, Omid Pournik3, Rose Allington3
1Department of Applied Health Sciences, University of Birmingham, UK.
Studies in health technology and informatics
|July 1, 2025
概括
像ChatGPT-4这样的大型语言模型 (LLM) 在从报告中提取甲状腺超声波U分数方面表现出高准确度. 这种人工智能工具与人类专家达成了97.7%的协议,提高了报告的一致性.
科学领域:
- 人工智能在医学中的应用
- 放射学报告自动化系统
- 自然语言处理应用程序
背景情况:
- 甲状腺超声波报告包含关键的U分类数据.
- 手动提取U分数是耗时的,容易变化.
- 大型语言模型 (LLM) 提供了从临床文本中自动提取数据的潜力.
研究的目的:
- 评估ChatGPT-4在从甲状腺超声报告中自动提取U分数方面的性能.
- 为了评估LLM提取的U分数与人类专家审查的准确性.
- 确定在放射学报告中使用LLM的挑战和潜在好处.
主要方法:
- 利用ChatGPT-4从14,248个未识别的甲状腺超声报告中提取U分数.
- 提供LLM与U分类系统指导方针.
- 将LLM提取的分数与初始临床医生和精细的专家分配的U分数进行比较.
主要成果:
- 在LLM提取和精制的人类U分数之间达到了97.7%的一致性.
- 在98.1%的多节结报告中成功确定了最高的U分数.
- 差异 (2.5%) 主要是由于描述模两可,报告复杂.
结论:
- 从甲状腺超声波报告中提取准确的U分数,LLM具有显著的潜力.
- 人工智能可以提高报告的一致性,减少手工工作量.
- 伦理考虑和工作流集成对于临床部署至关重要.
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