人工智能和机器学习在透析中的应用:对当前应用,挑战和未来方向的审查
Aanuoluwapo Clement David-Olawade1, Muyiwa Ademola Ogunbona2, Olabanke Florence Olawuyi3
1Endoscopy Unit, Glenfield Hospital, University Hospitals of Leicester, NHS Trust, Leicester, United Kingdom.
Clinica chimica acta; international journal of clinical chemistry
|February 17, 2026
概括
人工智能 (AI) 和机器学习 (ML) 正在彻底改变科和透析护理. 这些技术提供了更好的预测,诊断和治疗支持,但面临的采用挑战,如数据隐私和可解释性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在科中越来越重要,特别是在透析中.
- 多模式数据 (EHR,机器数据,实验室,成像) 促进了预后,诊断和治疗的先进工具.
- 透析中的AI/ML应用正在迅速发展,需要对当前的能力和未来方向进行审查.
研究的目的:
- 综合审查目前在透析护理中的AI/ML应用.
- 评估这些AI/ML工具的临床性能.
- 确定人工智能/透析ML的未来研究途径和挑战.
主要方法:
- 进行了对2020-2025年同行评审研究的叙述性审查.
- 在PubMed,Web of Science和其他主要数据库中进行文献搜索.
- 研究被按应用领域 (预测,CKD进展,决策支持,血管访问,NLP) 分类,并根据方法和性能进行分析.
主要成果:
- AI/ML的应用范围包括预测 (静脉分析性低血压,死亡率,住院),CKD进展,治疗优化 (贫血管理),血管访问监测 (图像分析) 和症状检测 (NLP).
- 报告了高性能指标,例如,AUROC用于IDH预测 (0.89-0.95) 和血管访问监测 (≈0.96),C指数高达0.83用于死亡率预测.
- 联邦学习 (FL) 正在出现,用于保护隐私的多中心协作.
结论:
- 通过提高精度和个性化治疗,AI/ML显示出显著的承诺,以提高透析护理.
- 数据隐私,模型解释性,监管障碍和数据代表性问题阻碍了广泛的临床采用.
- 需要进一步的研究来应对这些挑战,并促进AI/ML在例行透析实践中的整合.
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