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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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用共享利益共享数据:人工智能视角

Mohammad Tajabadi1, Linus Grabenhenrich2, Adèle Ribeiro1

  • 1Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, University of Marburg, Marburg, Germany.

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概括
此摘要是机器生成的。

这项研究提出了一种更公平的方法,用于医疗保健中的协作人工智能开发. 公司可以提供数据,并根据其输入获得相对比例的AI模型,鼓励参与并改进诊断工具.

关键词:
应用程序 应用程序 应用程序人工智能的人工智能是人工智能.人工智能模型的人工智能模型数据分析数据分析数据分析分享数据的数据共享.发展发展发展发展发展.诊断工具 诊断工具 诊断工具公平的公平的公平.联合学习的联合学习机器学习是机器学习.医疗数据 医疗数据工具 工具 工具 工具

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科学领域:

  • 医疗人工智能 医疗人工智能
  • 协作数据分析协作数据分析
  • 医疗信息学 医疗信息学

背景情况:

  • 开发强大的医疗保健人工智能模型需要来自多个中心的大量数据.
  • 数据隐私问题和专有限制限制了公共数据共享.
  • 现有的联合和群体学习方法缺乏私营部门合作的激励.

研究的目的:

  • 为合作医疗保健AI提供公平的数据贡献和AI模型所有权提出一种新的解决方案.
  • 激励公司参与数据共享和人工智能模型开发.
  • 确保从集体数据分析获得公平的利益,以改善诊断工具.

主要方法:

  • 探索一个新的协作数据分析和AI建模方法的观点.
  • 专注于一个系统,参与者可以获得与他们的数据贡献相称的AI模型.
  • 私营部门人工智能开发中的公平激励结构的概念框架.

主要成果:

  • 提出了一种解决协作人工智能公司有限激励的方法.
  • 基于数据贡献的AI模型好处的更公平分配的潜力.
  • 促进更多人参与医疗保健集体数据分析.人工智能.

结论:

  • 拟议的方法可以鼓励公司参与协作数据分析.
  • 更公平的数据贡献模型可以为所有参与者开发更好的诊断工具.
  • 这种观点为更公平,更有效的医疗保健人工智能发展提供了途径.