对于电生理学家来说,理解人工智能 (AI)
Charulatha Ramanathan1, Natalia A Trayanova2
1Carelog, 2945 Mokelumne Ct, Manteca, CA 95337, USA.
Indian pacing and electrophysiology journal
|February 1, 2026
概括
人工智能 (AI) 在临床电生理学中提供了许多应用,但其采用需要仔细评估. 本综述指导电生理学家理解人工智能工具,以实现负责任的临床整合和改善患者护理.
科学领域:
- 临床电生理学 临床电生理学
- 人工智能在医学中的应用.
背景情况:
- 人工智能 (AI) 越来越多地被整合到临床电生理学中,在心电图解释,心律失常检测和工作流程支持方面有应用.
- 人工智能方法,验证和整合的变化为临床使用创造了不确定性.
研究的目的:
- 为电生理学家提供评估和采用人工智能工具的实用指南.
- 支持人工智能在电生理学中的知情解释和负责任的临床整合.
主要方法:
- 从基于规则的系统到机器学习,深度学习和生成人工智能的AI演变.
- 检查核心概念:数据来源,标记,验证,分析性能与临床实用性.
- 分析常见的AI故障模式 (偏差,过拟合,可解释性) 和监管评估过程.
主要成果:
- 人工智能工具包括自动解释,心律失常检测,风险分层和程序规划.
- 关键考虑因素包括数据质量,验证严格性,以及将分析性能与临床效用区分开来.
- 监管许可具有特定的限制,临床医生保留对监督的责任.
结论:
- 电生理学家需要一个框架来解释AI证据并指导其评估和采用.
- 负责任的人工智能集成需要理解方法严谨性,潜在的故障模式和监管格局.
- 未来的人工智能方向包括个性化建模,决策支持,提高效率和获得护理.
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