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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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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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联合人工智能,当前状态和未来的潜在潜力

Phoebe Clark1, Eric K Oermann2,3, Dinah Chen4,5

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此摘要是机器生成的。

联合学习使医疗保健中的机器学习能够在不聚合敏感患者数据的情况下实现. 这种方法解决了开发准确的人工智能和医疗设备算法的数据隐私挑战.

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科学领域:

  • 医疗保健技术 医疗保健技术 医疗保健技术
  • 人工智能的人工智能是人工智能.
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 和人工智能 (AI) 在医疗保健和医疗设备方面越来越重要.
  • 开发精确的ML算法需要大量,多样化的数据集,由于患者数据的敏感性,这些数据很难获得.
  • 传统的数据聚合方法在医疗保健领域引发了严重的隐私问题.

研究的目的:

  • 提供联合学习 (FL) 作为保护隐私的ML技术的概述.
  • 探索FL在医疗保健中的当前应用,特别关注眼科.
  • 确定挑战,并概述FL在医疗应用中的未来方向.

主要方法:

  • 审查关于联合学习的现有文献.
  • 分析目前在医疗保健机构中FL的实施情况.
  • 来自眼科的案例研究例子.

主要成果:

  • 联合学习允许去中心化的模型培训,减轻直接患者数据共享的需要.
  • 在包括眼科在内的各种医疗保健领域证明了FL的成功应用,提高了诊断准确性.
  • 确定的主要挑战包括数据异质性,通信开销和监管障碍.

结论:

  • 联合学习为医疗保健中保护隐私的AI开发提供了可行的解决方案.
  • 需要进一步的研究和开发来克服当前的挑战,并在医疗器械和临床实践中扩大FL的采用.
  • 飞机具有很大的潜力,可以推进人工智能驱动的医疗保健创新,同时保护患者的机密性.