Qayyum Sardar N, Iftikhar Muhammad, Rehan Muhammad, Aziz Khan Gulmeena, Khan Maleeka, Naeem Risha, Ansari Rafay S, Ullah Irfan, Noori Samim
Department of Internal Medicine, Bacha Khan Medical College, Mardan, Pakistan.
Khyber Medical College, Peshawar, Pakistan.
Ann Med Surg (Lond). 2025 Jan 9;87(1):161-170. doi: 10.1097/MS9.0000000000002778. eCollection 2025 Jan.
Electrocardiography (ECG) remains a cornerstone of non-invasive cardiac diagnostics, yet manual interpretation poses challenges due to its complexity and time consumption. The integration of Artificial Intelligence (AI), particularly through Deep Learning (DL) models, has revolutionized ECG analysis by enabling automated, high-precision diagnostics. This review highlights the recent advancements in AI-driven ECG applications, focusing on arrhythmia detection, abnormal beat classification, and the prediction of structural heart diseases. AI algorithms, especially convolutional neural networks (CNNs), have demonstrated superior accuracy compared to human experts in several studies, achieving precise classification of ECG patterns across multiple diagnostic categories. Despite the promise, real-world implementation faces challenges, including model interpretability, data privacy concerns, and the need for diversified training datasets. Addressing these challenges through ongoing research will be crucial to fully realize AI's potential in enhancing clinical workflows and personalizing cardiac care. AI-driven ECG systems are poised to significantly advance the accuracy, efficiency, and scalability of cardiac diagnostics.
心电图（ECG）仍然是非侵入性心脏诊断的基石，但由于其复杂性和耗时性，人工解读存在挑战。人工智能（AI）的整合，特别是通过深度学习（DL）模型，通过实现自动化、高精度诊断，彻底改变了心电图分析。这篇综述重点介绍了人工智能驱动的心电图应用的最新进展，重点是心律失常检测、异常搏动分类以及结构性心脏病的预测。在多项研究中，人工智能算法，尤其是卷积神经网络（CNN），与人类专家相比显示出更高的准确性，能够对多种诊断类别的心电图模式进行精确分类。尽管前景广阔，但在实际应用中仍面临挑战，包括模型的可解释性、数据隐私问题以及对多样化训练数据集的需求。通过持续研究应对这些挑战对于充分实现人工智能在改善临床工作流程和个性化心脏护理方面的潜力至关重要。人工智能驱动的心电图系统有望显著提高心脏诊断的准确性、效率和可扩展性。
