在卫生系统规模上使用具有成本效益的大型语言模型的战略
Eyal Klang1,2, Donald Apakama3,4, Ethan E Abbott5,3,4
1Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. eyal.klang@mountsinai.org.
NPJ digital medicine
|November 18, 2024
概括
大型语言模型 (LLM) 可以简化临床工作流程,但扩展它们会带来挑战. 与Llama-3-70b和GPT-4-turbo-128k等高容量LLM同时连接多达50个任务,可以大大节省成本.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 计算健康 计算健康
背景情况:
- 大型语言模型 (LLM) 显示出优化临床工作流程的前景.
- 然而,在卫生系统规模上实施LLMs的经济和计算需求在很大程度上仍未得到审查.
- 了解这些限制对于有效的企业级采用至关重要.
研究的目的:
- 评估连接多个临床笔记和任务对在不同计算负载下LLM性能的影响.
- 评估不同LLM的准确性和输出格式化能力.
- 确定LLM利用的局限性,并探索成本效益的战略.
主要方法:
- 通过使用真实世界患者数据,评估了10个不同能力和大小的LLM.
- 进行了超过30万个实验,各种任务大小和配置.
- 测量了问题的答案准确性和输出格式化的可靠性.
主要成果:
- 随着问题和笔记数量的增加,模型性能下降.
- 高容量模型 (Llama-3-70b,GPT-4-turbo-128k) 展示了弹性,保持了高精度.
- 在50个具有大提示的任务后,GPT-4-turbo-128k的性能下降.
- 在这些模型中,连接多达50个同时执行的任务被证明是有效的.
- 经济分析显示,在50个任务中,成本降低了17倍.
结论:
- 在解决轻微故障后,LLM可以有效地连接多达50个同时执行的任务,特别是高容量模型.
- 这种方法大大降低了企业规模的医疗保健应用程序的成本.
- 该研究概述了LLM的利用限制,并突出了在医疗保健中成本效益高的部署途径.
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