微调大语言模型在CT协议分配中作为临床决策支持系统的有效性
Noriko Kanemaru1, Koichiro Yasaka2, Naomasa Okimoto1
1Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Journal of imaging informatics in medicine
|February 5, 2025
概括
一个微调的大型语言模型 (LLM) 有效地协助分配计算机断层扫描 (CT) 协议,提高放射科医生和临床决策支持住院医生的准确性和效率.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 精确的计算机断层扫描 (CT) 协议分配对于优化医学成像至关重要.
- 大型语言模型 (LLM) 作为CT协议的临床决策支持工具的潜力尚未确定.
研究的目的:
- 开发和评估一个精心调整的CT协议的LLM.
- 评估LLM的性能,独立和并发使用,在放射学工作流程中的有效性和效率.
主要方法:
- 一项回顾性研究使用了对比度增强的胸部和腹部CT检查 (2829/498/941用于培训/验证/测试).
- 输入包括临床指示,年龄和解剖覆盖范围;LLM对15个时代进行了微调.
- 在800个测试案例中评估了绩效,放射科医生和住院医生使用LLM作为决策支持工具.
主要成果:
- 该LLM实现了高精度 (顶-1:0.923,顶-2:0.963) 和宏观灵敏度 (0.907),在0.39秒内处理案例.
- 在LLM的使用中,居民 (0.913对0.936) 和放射科医生 (0.920对0.926) 的准确性得到了改进,居民 (p=0.02) 的准确性得到了显著改善.
- 读书时间在住院医生中减少了14%,在放射科医生中减少了12%.
结论:
- 精心调整的LLM显示了提高CT协议效率和诊断精度的巨大潜力.
- 临床医学仪器可以作为有效的临床决策支持系统,改善放射学工作流程.
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