人工智能在儿科重症监护神经病学中的使用现状和未来方向
Sameer Thadani1,2, Christopher M Horvat3, Christin Silos4
1Department of Pediatrics, Division of Critical Care Medicine, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA. Sameer.thadani@bcm.edu.
Pediatric nephrology (Berlin, Germany)
|November 25, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在儿科重症监护神经病学方面表现有前途,但临床实施落后于发展. 解决数据异质性和验证挑战是临床应用的关键.
科学领域:
- 儿科重症监护 腎病学
- 医疗人工智能 医疗人工智能
- 机器学习应用 机器学习应用
背景情况:
- 人工智能 (AI) 为推进医疗实践提供了巨大的潜力.
- 人工智能和机器学习 (ML) 在儿科重症监护神经病学的应用目前尚不发达.
- 非常需要评估AI/ML在这个专业领域的进展和挑战.
研究的目的:
- 从2000年到2024年,对儿童重症护理神经病学中的AI/ML算法进行范围审查.
- 为了确定AI/ML应用常见儿科病理的趋势.
- 评估AI/ML模型开发和临床实施之间的差距.
主要方法:
- 在儿科重症监护神经病学中对AI/ML研究进行系统的文献搜索.
- 提取有关病理,AI/ML技术和结果的数据.
- 分析研究设计,局限性和实施障碍.
主要成果:
- 确定了24篇文章,重点关注急性损伤 (AKI),电解质/酸障碍,液体管理和透析.
- 80%的研究集中在AKI预测/检测上;只有4%评估了临床影响.
- 显著的局限性包括单中心设计,缺乏外部验证,数据异质性和算法可移植性差.
结论:
- 在AI/ML模型开发和在儿科重症监护脏病学中床边实施之间存在很大的差距.
- 挑战包括儿科特定数据异质性,缺少算法可移植性和有限的可扩展性.
- 需要共同努力来弥合差距,并促进AI/ML在临床实践中的采用.
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