Naushad Zayera, Malik Jaya, Mishra Abhishek Kumar, Singh Shilpy, Shrivastav Dharmsheel, Sharma Chetan Kumar, Verma Ved Vrat, Pal Ravi Kant, Roy Biswajit, Sharma Varun Kumar
Department of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Department of Mathematics, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
High Blood Press Cardiovasc Prev. 2025 Sep 16. doi: 10.1007/s40292-025-00738-5.
Cardiovascular diseases (CVDs) continue to be the topmost cause of the worldwide morbidity and mortality. Risk factors such as diabetes, hypertension, obesity and smoking are significantly worsening the situation. The COVID-19 pandemic has powerfully highlighted the undeniable connection between viral infections and cardiovascular health. Current literature highlights that SARS-CoV-2 contributes to myocardial injury, endothelial dysfunction, thrombosis, and systemic inflammation, increasing the severity of CVD outcomes. Long COVID has also been associated with persistent cardiovascular complications, including myocarditis, arrhythmias, thromboembolic events, and accelerated atherosclerosis. Addressing these challenges requires continued research and public health strategies to mitigate long-term risks. Artificial intelligence (AI) is changing cardiovascular medicine and community health through progressive machine learning (ML) and deep learning (DL) applications. AI enhances risk prediction, facilitates biomarker discovery, and improves imaging techniques such as echocardiography, CT, and MRI for detecting coronary artery disease and myocardial injury on time. Remote monitoring and wearable devices powered by AI enable real-time cardiovascular assessment and personalized treatment. In public health, AI optimizes disease surveillance, epidemiological modeling, and healthcare resource allocation. AI-driven clinical decision support systems improve diagnostic accuracy and health equity by enabling targeted interventions. The integration of AI into cardiovascular medicine and public health offers data-driven, efficient, and patient-centered solutions to mitigate post-COVID cardiovascular complications.
心血管疾病（CVDs）仍然是全球发病和死亡的首要原因。糖尿病、高血压、肥胖和吸烟等风险因素正在显著恶化这种情况。新冠疫情有力地凸显了病毒感染与心血管健康之间不可否认的联系。当前文献表明，严重急性呼吸综合征冠状病毒2（SARS-CoV-2）会导致心肌损伤、内皮功能障碍、血栓形成和全身炎症，增加心血管疾病后果的严重程度。长期新冠还与持续性心血管并发症有关，包括心肌炎、心律失常、血栓栓塞事件和动脉粥样硬化加速。应对这些挑战需要持续的研究和公共卫生策略来降低长期风险。人工智能（AI）正在通过渐进式机器学习（ML）和深度学习（DL）应用改变心血管医学和社区卫生。人工智能增强风险预测，促进生物标志物发现，并改善诸如超声心动图、CT和MRI等成像技术，以便及时检测冠状动脉疾病和心肌损伤。由人工智能驱动的远程监测和可穿戴设备能够进行实时心血管评估和个性化治疗。在公共卫生领域，人工智能优化疾病监测、流行病学建模和医疗资源分配。人工智能驱动的临床决策支持系统通过实施有针对性的干预措施提高诊断准确性和健康公平性。将人工智能整合到心血管医学和公共卫生中，提供了以数据为驱动、高效且以患者为中心的解决方案，以减轻新冠后心血管并发症。
