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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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.
Cost Containment
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Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this; it...

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相关实验视频

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医学人工智能指标:一个参考资源

Ricardo A Gonzales1, Marcelo Straus Takahashi2, Tara Retson3

  • 1Balliol College, Radcliffe Department of Medicine, Oxford University, Oxford, UK.

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一个新的框架标准化了临床医学的人工智能 (AI) 性能指标. 这种AI指标分类学增强了医疗保健应用中的模型评估,比较和偏差检测.

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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
  • 本体学 工程学 工程学

背景情况:

  • 将人工智能 (AI) 整合到临床医学中需要强有力的绩效评估.
  • 目前缺乏标准化指标阻碍了人工智能的可复制性和在医疗保健中的采用.
  • 需要一个全面的,机器可读的AI性能指标框架.

研究的目的:

  • 提出一种机器可解释的框架,用于标准化AI性能指标.
  • 为207个人工智能性能指标正式命名和描述.
  • 为了支持人工智能数据集,模型和项目的放射学本体 (ROADMAP).

主要方法:

  • 开发了人工智能评估指标 (图形,矩阵,标量) 的综合分类.
  • 包括指标定义,引用,同义词,公式和界限.
  • 使用逻辑公理将18个AI性能标准的指标链接起来.

主要成果:

  • 建立了一个结构化的表示,捕捉AI评估指标语义.
  • 分类学支持各种数据类型:结构化数据,图像,音频和文本.
  • 启用了人工智能模型报告的自动完整性检查.

结论:

  • ROADMAP分类统一了人工智能指标术语,以便更好地进行模型比较.
  • 促进偏差检测和选择适当的评估方法.
  • 提高AI模型报告在临床医学中的透明度和可靠性.