数据集成和系统互操作性:麻醉学人工智能的先决条件.
Olivier Duranteau1,2, Benjamin Popoff3
1Department of Anesthesiology and Pain Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada.
Current opinion in anaesthesiology
|February 13, 2026
概括
整合麻醉学数据是人工智能 (AI) 采用的关键. 目前的系统阻碍了人工智能翻译,原因是数据集成不良,而不是算法性能.
科学领域:
- 在外科手术期间的医学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 麻醉学产生了庞大的,异构的外科手术期间数据,包括生理信号和临床文档.
- 尽管数据丰富,人工智能 (AI) 系统的临床部署是有限的.
- 现有的系统往往缺乏互操作性,阻碍了人工智能集成到实践中.
研究的目的:
- 审查数据集成和系统互操作性限制如何限制人工智能在麻醉学中的翻译.
- 识别人工智能在外科护理中采用的障碍.
- 为在麻醉学中启用人工智能的解决方案提出建议.
主要方法:
- 关于麻醉学中人工智能的最新研究的文献综述.
- 在周围操作数据系统中分析数据整合的挑战.
- 检查互操作性标准及其实施.
主要成果:
- 在麻醉学中采用人工智能的首要障碍是数据集成不足,而不是算法性能.
- 数据被分散在各种系统 (AIMS,EHR,设备) 中,使用可变模型和同步.
- 新兴标准 (HL7 FHIR,通用数据模型) 提供框架,但部分实施.
结论:
- 在麻醉学中有效地部署人工智能需要互操作的,高质量的数据基础设施.
- 标准化数据模型,语义协调和时间对齐至关重要.
- 强大的数据治理是可扩展,可靠的AI支持的外科护理的先决条件.
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