在重症监护医学的机器学习和人工智能:从基于规则的系统到前沿模型的关键重新校准
Pierre Hadweh1, Alexandre Niset1,2, Michele Salvagno3
1Sciense, New York, NY 10027, USA.
Journal of clinical medicine
|June 26, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在加强关键护理决策支持. 虽然对ICU效率有希望,但像偏见和透明度这样的挑战需要谨慎管理以确保安全实施.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学医学信息学
- 人工智能的人工智能是人工智能.
背景情况:
- 重症监护病房 (ICU) 产生大量实时数据,对临床决策构成挑战.
- 临床决策支持系统 (CDSS) 正在与AI和ML一起发展,以管理复杂的ICU数据.
研究的目的:
- 追踪机器智能在重症监护中的演变.
- 在关键ICU领域审查AI/ML应用.
- 确定在ICU中人工智能的挑战和未来方向.
主要方法:
- 综述AI/ML在重症监护中的历史发展.
- 在早期预警系统,败血症管理,机械通风和诊断中分析AI应用.
- 讨论人工智能实施的当前局限性和未来要求.
主要成果:
- 人工智能/机器学习已经从基于规则的系统发展到复杂的模型,包括边界模型.
- 应用程序涵盖了诸如预测分析和工作流优化等关键领域.
- 实施受到透明度,整合,偏见和监管等问题的阻碍.
结论:
- 人工智能/ML为改善ICU结果和效率提供了巨大的潜力.
- 解决道德,监管和技术方面的挑战对于安全和有效的部署至关重要.
- 未来的成功依赖于经过验证,以人为中心的系统,协作和持续监控.
关键词:
人工智能的人工智能是人工智能.临床决策支持系统 临床决策支持系统关键护理关键护理的关键护理深度学习是一种深度学习.重症监护医学的重症监护医学的医药.大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.预测分析 预测分析隐私 隐私 隐私 隐私 隐私 隐私在安全方面,安全是安全的.这是一种血症.更多相关视频
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