在大型语言模型中以利益相关者为中心的参与增强了卫生系统的功能
Zhiyuan Wang1, Runze Yan2, Sherilyn Francis3
1School of Engineering and Applied Science, University of Virginia, Charlottesville, VA USA.
大型语言模型 (LLM) 可以改善医疗保健,但利益相关者的参与是关键. 参与式方法和LLM技术共同确保LLM增强的卫生系统满足实际和道德需求.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 具有重大潜力,可以彻底改变医疗保健服务,影响临床决策支持,患者护理和行政流程.
- 然而,成功和可持续地将LLM整合到医疗保健系统中受到参与性差距的挑战,可能导致系统与利益相关者需求和道德考虑失调.
研究的目的:
- 探索LLM增强型卫生系统 (LLMHS) 在整个开发生命周期中应用参与式方法.
- 论证参与式方法在吸引各种利益相关者参与LLMHS发展中的关键作用.
- 研究LLM技术如何促进新的参与机会,增强利益相关者的参与,推动技术创新.
主要方法:
- 在医疗保健LLM开发的背景下,对参与式设计原则的文献综述和概念分析.
- 检查现有和潜在的LLM应用程序,以促进利益相关者的互动和反循环.
- 在LLMHS生命周期中整合参与方法的框架开发.
主要成果:
- 参与式方法对于将LLMHS与医疗保健利益相关者的实际和道德要求保持一致至关重要.
- 可以利用LLM技术创建创新的参与方法,加深利益相关者的参与.
- 参与式方法与医疗保健中的LLM技术进步之间存在协同关系.
结论:
- 通过LLM增强的卫生系统需要强大的参与策略,以确保与现实世界医疗需求和道德标准保持一致.
- 参与式方法和LLM创新的整合可以导致下一代医疗保健系统的发展.
- 未来的研究应该集中在这些综合方法的经验验证和实施上.
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