关键护理神经病学中的人工智能:当前应用,新兴技术和临床整合的挑战
Wisit Cheungpasitporn1, Charat Thongprayoon1, Kianoush Kashani1,2
1Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Kidney360
|October 28, 2025
概括
人工智能 (AI) 在重症监护脏病学 (CCN) 中显示出有前景,以改善预测和诊断. 需要进一步的研究来确认其对患者结果的影响,并克服采用障碍.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能的人工智能
- 关键护理医学 关键护理医学
背景情况:
- 人工智能 (AI) 在重症监护科 (CCN) 中提供了潜在的进步.
- 目前的人工智能应用包括急性损伤预测和透析管理,往往优于传统方法.
- 人工智能对以患者为中心的结果,如死亡率和成本效益的影响尚未完全理解.
研究的目的:
- 审查CCN中AI应用的当前证据.
- 识别新兴的人工智能技术及其潜在好处.
- 概述在CCN中推进AI的优先事项.
主要方法:
- 文献综述综合了CCN中关于人工智能的当前证据.
- 讨论新兴的人工智能技术,如合规预测和因果推理.
- 在临床实践中识别AI采用障碍的障碍.
主要成果:
- 人工智能应用在CCN的预测,诊断和工作流程优化方面表现有前途.
- 新兴技术提高了可靠性,可解释性和个性化护理.
- 挑战包括数据异质性,有限的验证,警报疲劳和经济限制.
结论:
- 专注于临床结果的严格的多中心验证至关重要.
- 整合不确定性量化和因果模型是必要的.
- 开发以临床医生为中心的接口和透明的监管框架是CCN人工智能的主要优先事项.
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