用于CT和MRI协议的人工智能:对传统机器学习,BERT和大型语言模型的元分析.
Ethan Sacoransky1, Yash Chauhan2, Scott J Adams1
1Department of Medical Imaging, Royal University Hospital, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
AJR. American journal of roentgenology
|October 29, 2025
概括
人工智能 (AI) 模型,包括传统的机器学习 (ML),BERT和大语言模型 (LLM),显示出对自动化CT和MRI检查协议的承诺. 精心调整的BERT模型在对23项研究的元分析中显示出最高的性能.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 机器学习应用 机器学习应用
背景情况:
- 对CT和MRI的检查协议是一个劳动密集的过程.
- 人工智能 (AI) 为自动化协议任务提供了潜在的解决方案.
研究的目的:
- 评估传统机器学习 (ML) 模型,BERT模型和用于自动化CT和MRI协议的大型语言模型 (LLM) 的性能.
- 为了比较不同的AI方法在分配成像协议时的准确性.
主要方法:
- 在多个数据库 (MEDLINE,Embase,Scopus,Web of Science,IEEE Xplore,Google Scholar) 进行了系统的文献搜索,截至2025年7月.
- 包括的研究报告了基于AI的CT/MRI编程技术的性能指标.
- 随机效应元分析被用来汇集准确性结果,与威尔士t测试进行比较.
主要成果:
- 该分析包括23项研究,涵盖了超过119万个成像要求.
- 总体而言,人工智能编程的准确性为85%. 传统的ML,BERT和LLM的聚合精度分别为83%,87%和86%,AI类型之间没有显著差异.
- 精心调整的BERT模型,特别是BioBert,获得了最高的准确性 (93%),在十大表现最好的模型中,有六个基于BERT.
结论:
- 精心调整的BERT模型代表了自动CT和MRI协议的领先AI方法.
- 人工智能工具具有显著的潜力,可以简化放射科医生的工作流程,可能是通过混合人工智能-放射科医生合作.
- 进一步研究为此应用微调的LLM是有必要的.
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