人工智能在ECG解读与临床应用

刘霞

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人工智能在ECG解读与临床应用

特约专稿 | 更新时间：2026-01-06

- 人工智能在ECG解读与临床应用
- Artificial Intelligence in ECG Interpretation and Clinical Application
- 临床心电学杂志 2025年34卷第6期页码：411-413
- 作者机构：
  
  200025 上海，上海交通大学医学院附属瑞金医院心脏科
- 作者简介：
  
  [ "刘霞，上海交通大学医学院附属瑞金医院心脏科主任医师。主要临床工作是心电学和心律失常药物治疗，在临床工作中积累了大量的心电学诊断和心律失常药物治疗的经验和资料。独立主编心电图图谱5 部。曾任：中国水利水电医学科学技术学会心电学分会副主委中国医疗保健国际交流促进会心律与心电分会常务委员中国医药生物技术协会心电学技术分会常务委员中华医学会心电生理和起搏分会心电学组委员临床心电图杂志常务编委实用心电学杂志常务编委心电学杂志常务编委" ]
- 基金信息：
- DOI：
  中图分类号： R541.7　R540.4
- 收稿：2025-11-15，
  
  纸质出版：2025-12-28
- 稿件说明：
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刘霞. 人工智能在ECG解读与临床应用[J]. 临床心电学杂志, 2025, 34(6): 411-413.

LIU Xia. Artificial Intelligence in ECG Interpretation and Clinical Application[J]. J Clin Electrocardiol, 2025, 34(6): 411-413.
刘霞. 人工智能在ECG解读与临床应用[J]. 临床心电学杂志, 2025, 34(6): 411-413. DOI：

LIU Xia. Artificial Intelligence in ECG Interpretation and Clinical Application[J]. J Clin Electrocardiol, 2025, 34(6): 411-413. DOI：

摘要

一个多世纪以来，心电图在诊断心律失常、缺血/梗死及心肌肥厚方面的价值无可争议。在当前临床实践中，为确保诊断安全性，采用计算机辅助分析的诊断仍需医师进行最终复核判读。人工智能在心电图判读演进中的真正意义，远不止于实现自动化。人工智能正不断展现并验证其在心电图和发现心脏病变迹象方面的价值。在可预见的未来，人工智能应被设计用于增强而非取代人类的临床判断。

Abstract

For over a century

the electrocardiogram (ECG) in diagnosing arrhythmias

ischemia/infarction

and hypertrophy is undisputed. In current clinical practice

the diagnosis with computerized interpretation algorithms

still necessitate a final physician re-read to ensure diagnostic safety. The true significance of artificial intelligence (AI) in the evolution of ECG interpretation extends far beyond mere automation. AI is seeing and validating its value in ECG interpretation and detecting signs of heart disease. For the foreseeable future

AI should be designed to augment

not automate

human clinical judgment.

关键词

Keywords

references

YEHUALASHET MA , FRIEDHELM S , BISRAT DD , et al . Interpretable machine learning techniques in ECG-based heart disease classification [J ] : A systematic review , 2022 , 13 ( 1 ): 111 . DOI: 10.3390/diagnostics13010111 http://dx.doi.org/10.3390/diagnostics13010111 .

Jeff Healey . AI is better than humans at analyzing long-term ECG recordings . Nature Medicine , 10 Feb 2025.

The Lancet: Deep learning AI may identify atrial fibrillation from a normal rhythm ECG . News Release 1 Aug 2019 .

MIN S L , TAE GS , YOUNGJOO L , et al . Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study . Eur Heart J , 2025 , 46 ( 20 ): 1917 - 1929 .

Anthony C . P. AI and ECG Interpretation: Insights and Trends for Today's Cardiologists . GE HealthCare Oct 06 , 2021 .

PIERRE ELIAS , SNEHA SJ , TIMOTHY POTERUCHA , et al . Artificial Intelligence for Cardiovascular Care—Part 1: Advances: JACC Review Topic of the Week [J ] . JACC , 2024 , 83 ( 24 ): 2472 - 2486 . DOI: 10.1016/j.jacc.2024.03.400 http://dx.doi.org/10.1016/j.jacc.2024.03.400 .

FDA Grants Breakthrough Device Designation for HeartSciences' AI-ECG Algorithm News Heart Valve Technology . June 12 , 2025 .

CHIN LIN , FENG-CHIH KUO , TOM CHAU , et al . Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction [J ] . Commun Med (Lond) , 2024 , 4 ( 1 ): 42 . DOI: 10.1038/s43856-024-00472-4 http://dx.doi.org/10.1038/s43856-024-00472-4 .

ROHAN KHERA , EVANGELOS K.O , GIRISH NN , et al . Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review [J ] . JACC , 2024 , 84 ( 1 ): 97 - 114 . DOI: 10.1016/j.jacc.2024.05.003 http://dx.doi.org/10.1016/j.jacc.2024.05.003 .

10. SAU A , PASTIKA L , SIELIWONCZYK E , et al . Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study [J ] . Lancet Digit Health , 2024 , 6 ( 11 ): e791 - e802 .

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