错误的心电图解读及其临床影响
Ayesha Shaik1,2, Nirav Patel1,2, Chikezie Alvarez1,2
1Division of Cardiology, Hartford Hospital, Hartford, Connecticut, USA.
Journal of cardiovascular electrophysiology
|June 5, 2023
概括
人工智能 (AI) 误解心电图 (ECG) 发生在2.0%的病例中,导致不适当的治疗和增加医疗保健成本. 医生必须仔细审查AI解释的心电图,特别是对心房动 (AF).
科学领域:
- 心脏病学 心脏病学
- 医疗人工智能 医疗人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能 (AI) 协助心电图 (ECG) 解释,以快速分组患者.
- 人工智能误解可能会误导临床医生,可能会影响患者管理.
- 了解AI心电图误解的发生率和临床意义至关重要.
研究的目的:
- 为了确定人工智能对心电图误解的频率.
- 评估这些误解对患者管理的临床影响.
- 为了确定最容易导致AI误诊的特定心律失常.
主要方法:
- 从2020年5月到2021年5月,对4969个ECG进行了回顾性分析.
- 诊断为心房动 (AF),鼻高心率 (ST),鼻胆心率 (SB),心室内导电延迟 (IVCD) 和心房过早收缩 (PAC) 的心电图进行了选.
- 误解的心电图被分类,并评估它们对药物使用和咨询的影响.
主要成果:
- 101个心电图 (2.0%) 被误解,错误诊断的AF是最常见的 (58.4%).
- 误解导致不适当使用β-阻断剂 (19.8%),CCB (5.0%),抗不律性药物 (7.9%) 和抗凝剂 (6.9%).
- 报告指出,心脏病 (41.6%) 和电生理学 (8.9%) 咨询不合适.
结论:
- 人工智能对心电图的误解可能会导致低于最佳的医疗疗法和医疗资源利用率的增加.
- 医生应该保持谨慎,并彻底审查AI解释的心电图,特别是那些表明AF的心电图.
- 需要加强警,以减轻与心脏病学中人工智能驱动的诊断工具相关的风险.
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