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评估实施新人工智能选工具的风险 - - 在一个已经有风险的世界里,有多少风险是合理的?

Alexa Nord-Bronzyk1, Julian Savulescu1,2, Angela Ballantyne3

  • 1Centre for Biomedical Ethics, National University of Singapore, Singapore.

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概括
此摘要是机器生成的。

应急医学中的人工智能 (AI) 风险预测工具面临着实施挑战. 风险效益分析支持紧急风险预测评分 (SERP) 工具的持续评估策略,与学习卫生系统保持一致.

关键词:
人工智能的人工智能是人工智能.实施 实施 实施风险 风险 风险 风险 风险测量三级是什么意思

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科学领域:

  • 紧急医疗 紧急医疗
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 紧急医疗 (EM) 在风险预测方面面临着独特的挑战,原因是紧急和高压环境.
  • 人工智能 (AI) 工具旨在简化分拣和缓解紧急部门 (ED) 过度拥挤等问题.
  • 实施风险包括过量/不足选,假阳性和潜在的医疗保健专业偏见,使人工智能工具的采用复杂化.

研究的目的:

  • 探索与紧急医疗中的AI风险预测工具相关的风险.
  • 分析新加坡机器学习分类工具Score for Emergency Risk Prediction (SERP) 的案例研究.
  • 通过风险效益分析和学习健康系统框架来评估实施战略的道德适当性.

主要方法:

  • 分析涉及紧急风险预测 (SERP) 工具的案例研究.
  • 拟议实施战略的风险效益分析.
  • 考虑正在进行的评估研究作为实施前随机对照试验 (RCT) 的替代方案.

主要成果:

  • 实施前的RCT被认为是SERP不可行的,因为它与临床判断的相互作用.
  • 作为评估SERP现实世界的能力的一种方法,建议进行持续评估研究.
  • 风险效益分析表明,拟议的实施战略在道德上是合适的.

结论:

  • 对SERP的实施策略与以改进为重点和系统方法保持一致.
  • 学习健康系统 (LHS) 框架对于确保安全,有效性和持续学习至关重要.
  • 伦理考虑和风险减轻对于成功整合紧急医疗中的AI工具至关重要.