Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

中文翻译：

---

人工智能增强心电图准确诊断和管理心血管疾病

经过人工智能（AI）改进的心电图（ECG）已成为精确诊断和治疗心血管疾病的潜在技术。传统心电图是一种常用、廉价且易于获取的测试，可提供有关心脏生理和解剖状态的重要信息。然而，根据翻译人员的培训水平和经验，人类对心电图的解释可能会有所不同，这可能会使诊断变得更加困难。使用人工智能，尤其是深度学习卷积神经网络 (CNN) 来查看单个、连续和间歇性心电图导联，从而产生了完全自动化的人工智能模型，可以像人类一样解释心电图，而且可能更准确、更一致。这些人工智能算法是心血管疾病的有效非侵入性生物标志物，因为它们可以识别心电图中人类解读者可能不易察觉的微妙模式和信号。在心电图分析中使用人工智能有几个好处，包括快速准确地检测心律失常、隐性心脏病和左心室衰竭等问题。它有潜力帮助医生进行解释、诊断、风险评估和疾病管理。除此之外，人工智能增强的心电图已被证明可以促进心力衰竭和其他心血管疾病的识别，特别是在急诊室环境中，从而可以提供更快、更精确的治疗选择。然而，尽管人工智能具有潜力，但它在心脏病学中的应用仍存在一些局限性和障碍。人工智能心电图分析的有效实施受到系统偏差等问题的限制。基于年龄、性别和种族的偏见是由不平衡的数据集造成的。当不同的人口统计数据代表性不足时，模型的性能就会受到影响。训练集中年龄数据的偏差可能会导致与年龄相关的心电图变化被忽视。心电图模式受性别生理差异的影响；倾向于某一性别的数据集可能会损害其他性别的准确性。遗传变异会影响心电图读数，因此数据集中的种族多样性非常重要。此外，泛化不足、监管障碍和可解释性问题等问题也导致了部署困难。当应用于不同人群时，模型缺乏稳健性常常阻碍其实际适用性。监管要求所需的详尽验证会导致部署延迟。无法解释的困难模型会削弱临床医生的信心。多样化的数据集管理、偏差缓解策略、跨人群的持续验证以及监管批准的协作努力对于在临床环境中成功部署 AI ECG 至关重要，并且必须采取措施解决这些问题。为了保证在临床实践中安全、成功的部署，人工智能在心脏病学中的使用必须充分了解算法及其局限性。总之，人工智能增强心电图通过提供准确、及时的诊断见解、帮助临床医生和改善患者的治疗结果，在改善心血管疾病的管理方面具有巨大的潜力。随着技术的不断进步，需要进一步的研究和开发，以充分实现人工智能改善心脏病学实践和患者护理的承诺。