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在败血症管理中优化人工智能:当前的机遇,并密切关注未来
Darragh O'Reilly1, Jennifer McGrath1, Ignacio Martin-Loeches1,2
1Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James' Hospital, Dublin, Ireland.
Journal of intensive medicine
|January 24, 2024
概括
人工智能 (AI) 可以帮助更早地诊断败血症并预测器官衰竭,改善患者的治疗结果并降低医疗保健成本. 然而,数据多样性和临床医生信任等挑战阻碍了AI在败血症管理中的采用.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 败血症是一个日益增长的全球健康挑战,发病率越来越高.
- 败血症的延迟识别和治疗导致死亡率更高.
- 人工智能 (AI) 正在彻底改变医疗保健的提供.
研究的目的:
- 审查人工智能如何增强败血症诊断和管理策略.
- 探索AI在预测败血症和器官衰竭方面的潜力.
- 讨论人工智能在败血症临床实践中的好处和局限性.
主要方法:
- 审查当前关于AI在败血症中的应用文献.
- 对用于分析临床数据的AI和机器学习 (ML) 算法的分析.
- 讨论人工智能对败血症和器官衰竭的预测能力.
主要成果:
- 人工智能/ML算法可以分析复杂的临床数据,用于早期的败血症诊断和治疗.
- 人工智能模型显示出在临床表现之前预测败血症风险和器官衰竭的潜力.
- 人工智能实施通过工作流效率和更好的结果提供了潜在的成本节约.
结论:
- 人工智能为改善败血症护理,降低死亡率和降低成本提供了巨大的潜力.
- 人工智能采用的障碍包括缺乏多样化的数据集,算法复杂性和监管障碍.
- 制定强有力的政治和监管框架对于临床医生和患者对用于败血症管理的AI的信任至关重要.
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