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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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人工智能用于非侵入性健康诊断

P R Wankhede1, Devendra Bhuyar1, Shrinivas Zanwar2

  • 1Department of Electronics and Computer Engineering, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar 431011, Maharashtra, India.

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概括

人工智能 (AI) 和机器学习 (ML) 通过分析复杂数据进行实时监控来增强非侵入性诊断. 本综述探讨了AI/ML应用,挑战和改善医疗保健诊断的未来方向.

关键词:
人工智能的人工智能是人工智能.生物传感器生物传感器临床决定是临床决定.诊断 诊断 诊断 诊断 诊断 诊断机器学习是机器学习.这是一种非侵入性的方法.传感器 传感器 传感器可以穿戴的可穿戴设备.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 非侵入性诊断对于早期疾病检测和患者护理至关重要,但往往缺乏敏感性和及时解释.
  • 人工智能 (AI) 和机器学习 (ML) 通过识别数据中的复杂模式来提供解决方案,从而实现持续监控.

研究的目的:

  • 审查AI/ML在各种非侵入性诊断平台上的整合.
  • 突出新兴的AI/ML技术,并讨论在医疗保健中采用它们的障碍.

主要方法:

  • 对AI/ML在非侵入性诊断中的应用进行了全面的文献综述.
  • 探索各种平台:医学成像,可穿戴传感器,呼吸分析,生物流体分析和光学传感.
  • 讨论先进的AI技术,如联合学习,可解释的AI,数字双胞胎和纳米传感器.

主要成果:

  • 人工智能和ML的整合正在将诊断从临时评估转变为持续的实时监测.
  • 新兴方向包括联合学习,可解释的AI,数字双胞胎和纳米传感器.
  • 采用存在重大障碍,包括数据隐私,算法公平性,监管问题和系统集成.

结论:

  • 人工智能/ML对推进非侵入性诊断,提高灵敏度,特异性和可访问性具有巨大的前景.
  • 克服采用障碍对于将AI创新转化为可扩展,具有成本效益,以患者为中心的医疗保健解决方案至关重要.
  • 本综述为研究人员,临床医生和政策制定者提供了一份路线图,以引导人工智能辅助非侵入性诊断的未来.