大型语言模型的经济学和公平性:医疗保健视角
Radha Nagarajan1, Midori Kondo2, Franz Salas3
1Children's Hospital of Orange County, Orange, CA, United States.
Journal of medical Internet research
|November 14, 2024
概括
选择正确的大型语言模型 (LLM) 途径对于医疗保健采用至关重要. 本分析比较了从头开始的培训,微调和开箱即用的模型,考虑了公平实施的成本,风险和益处.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 大型语言模型 (LLM)
背景情况:
- 大型语言模型 (LLM) 在医疗保健方面越来越有前途,但成功的采用取决于数字准备,基础设施,劳动力培训,隐私和监管框架.
- 医疗生态系统面临着独特的挑战和变化,影响了LLM实施和公平获取的最佳途径.
研究的目的:
- 讨论和比较三个不同的LLM实施途径:从头开始培训 (TSP),微调途径 (FTP) 和开箱途径 (OBP).
- 分析这些途径在主要云提供商 (亚马逊,微软,谷歌,甲骨文) 的风险,好处和经济效益.
- 指导卫生系统在战略性地采用LLM,以实现公平获取和改善医疗保健结果.
主要方法:
- 对三个LLM实施途径 (TSP,FTP,OBP) 的比较分析.
- 检查经济因素,包括跨云服务提供商的按需和现货定价.
- 讨论管理服务和云计算企业工具的实用性,例如为医疗保健数据提供联合学习.
主要成果:
- 从零开始的培训 (TSP) 提供最大的定制性和性能,但也是最资源密集的.
- 微调路径 (FTP) 平衡了定制,成本和性能,尽管预训练模型可能会引入偏差.
- 准备就绪的路径 (OBP) 允许在最小的定制和透明度下快速部署,从而带来潜在的长期可用性和集成挑战.
结论:
- 选择LLM实施途径 (TSP,FTP,OBP) 取决于具体的卫生系统需求和可负担性.
- 管理服务和企业工具可以减轻LLM实施的挑战,特别是敏感的医疗保健数据.
- 对途径经济学和权衡的战略理解对于成功的LLM采用,价值展示和对医疗保健结果的积极影响至关重要.
关键词:
在法学士 (LLM) 课程中.这是一个云云云.云服务提供商 云服务提供商民主化 民主化 民主化经济学 经济学 经济学股权资本 股权资本医疗保健 医疗保健 医疗保健健康结果健康结果.实施实施实施实施实施.大型语言模型更多相关视频
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