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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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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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在紧急医疗中的可解释的人工智能:概述

Yohei Okada1,2, Yilin Ning3, Marcus Eng Hock Ong1,4

  • 1Health Services and Systems Research, Duke-NUS Medical School, Singapore.

Clinical and experimental emergency medicine
|November 28, 2023
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概括

可解释的人工智能 (AI) 通过澄清其功能,提高了对应急医学的AI的信任. 这种方法解决了"黑子"问题,改善了AI和机器学习 (ML) 工具的临床采用.

关键词:
人工智能的人工智能是人工智能.紧急医疗 紧急医疗机器学习 机器学习复苏 复苏 复苏 复苏 复苏 复苏

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 紧急医疗 紧急医疗

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 在紧急医疗护理中提供了变革性的潜力,包括改进的分拣,诊断和预后.
  • 临床医生对人工智能的信任受到人工智能专业知识的缺乏所阻碍,这往往导致人工智能被视为不可破解的"黑子".
  • 可解释AI (XAI) 对于弥合这一知识差距至关重要,使临床医生能够理解和信任AI功能.

研究的目的:

  • 定义和阐明可解释AI (XAI) 在紧急医疗环境中的重要性和作用.
  • 概述在临床紧急情况下实施AI和ML所面临的挑战.
  • 促进临床医生,开发人员和研究人员之间的合作,以实现有效的AI集成.

主要方法:

  • 介绍了人工智能模型中可解释性,可解释性和透明性的定义.
  • 在临床环境中XAI的必要性是通过理由,控制,改进和发现的理由来证明的.
  • 描述了三个类别的可解释性:预建模,可解释模型和后建模,为后建模技术提供了示例,如可视化和特征相关性.

主要成果:

  • 可解释的人工智能对于临床证明至关重要,它可以更好地控制人工智能驱动的决策,促进模型改进,并有助于新发现.
  • 后建模可解释性技术,如可视化和特征相关性,提供了解AI输出的实际方法.
  • 在紧急医疗中成功实施AI/ML需要克服与整合和促进跨学科合作相关的挑战.

结论:

  • 可解释的人工智能对于增强临床医生的信任和促进在紧急医疗中采用AI和ML工具至关重要.
  • 了解XAI概念,包括其不同类型和方法,使急诊医学临床医生能够更有效地利用AI.
  • 临床医生,开发人员和研究人员之间的合作对于克服实施障碍和最大限度地利用人工智能在紧急护理中的好处至关重要.