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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.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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开发医学人工智能研究中心.

Curtis P Langlotz1, Johanna Kim2, Nigam Shah3

  • 1Departments of Radiology, Medicine, and Biomedical Data Science, Center for Artificial Intelligence in Medicine and Imaging, Stanford University.

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概括

斯坦福大学 斯坦福大学

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科学领域:

  • 生物医学研究的研究.
  • 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:

  • 人工智能 (AI) 和机器学习 (ML) 正在改变生物科学,医学学术和临床护理.
  • 学术医学中心正在建立专门的部门来促进AI/ML研究和创新.

研究的目的:

  • 为斯坦福大学的一个AI/ML研究中心展示一个成功的模型.
  • 概述在学术医学环境中支持AI/ML研究的关键策略.

主要方法:

  • 在学术领袖,临床部门,赠款和行业合作伙伴的支持下建立一个AI/ML研究中心.
  • 实施四种关键策略:基于项目的学习,内部资助,数据基础设施和教育/开放数据计划.
  • 促进临床医生,计算机科学家和数据科学家之间的跨学科合作.

主要成果:

  • 医学和成像人工智能中心 (AIMI) 成功支持AI/ML研究.
  • 该模型强调互补的基础研究和应用研究.
  • 该中心有助于创建大型,多式联络,AI-ready的临床数据集.

结论:

  • 多学科的卓越中心对于在学术医学中心推进AI/ML至关重要.
  • 这些中心为负责任,道德和公平的AI/ML实施提供了基础.
  • 整合多元专业知识的团队科学对于使用AI/ML解决复杂的生物医学问题至关重要.