放射学程序的概念验证提示大型语言模型请求路由
Brian P Triana1, Walter F Wiggins2, Nicholas Befera1
1Department of Radiology, Duke University Medical Center, Durham, North Carolina.
Journal of vascular and interventional radiology : JVIR
|March 26, 2025
概括
一个大型语言模型 (LLM) 准确地路由了医院程序请求,证明了提高临床沟通和工作流程效率的成本效益解决方案.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床工作流的优化 临床工作流优化
背景情况:
- 在大型学术医院中,程序请求的有效路由至关重要.
- 目前的方法可能是手动的或复杂的,导致延迟和错误.
- 大型语言模型 (LLM) 提供了自动化和改进通信任务的潜力.
研究的目的:
- 评估提示的LLM对路由程序请求的准确性和成本效益.
- 评估LLM在将请求指向适当的医院团队,电话号码或呼叫器方面的表现.
主要方法:
- 根据现有的医院团队,时间表和联系信息,制定了基于文本的程序请求路由规则.
- 使用GPT-3.5-turbo和GPT-4以及四个开放重量模型创建了一个提示的LLM.
- 在250个范围内的和25个范围之外的程序请求上测试了LLM.
主要成果:
- 在范围内的请求中,GPT-4实现了96.4%的准确性,在范围之外的请求中达到76.0%,超过了其他模型 (P < .001).
- 所有模型在晚上和周末时间表现下降 (P < .001).
- API的成本很低:GPT-4的需求量约为0.03美元,GPT-3.5-turbo的需求量约为0.0006美元.
结论:
- 提示式LLM对于学术医院的路由程序请求是准确且具有成本效益的.
- 这项技术可以帮助临床医生导航复杂的通信系统,如放射学电话树.
- 对于阅读室协调员来说,LLM可以成为有价值的工具,以提高请求路由效率.
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