Comprehensive Summary
This article reviews how AI is being leveraged to address cardiovascular health challenges that have been exacerbated since the COVID-19 pandemic. The authors conduct a narrative and literature review, synthesizing and discussing published studies on machine learning, deep learning, imaging and data analysis for echocardiography, CT, and MRI scans to detect coronary artery disease and myocardial injury, wearable devices, and clinical decision making and support systems applied to cardiovascular disease and post-COVID complications in patients. Remarkably, AI models were demonstrated to have strong capabilities in risk prediction and biomarker discovery, enabling earlier detection of cardiovascular complications via imaging, lab data, or wearable sensors. The authors also argue that AI-driven clinical decision support systems and remote monitoring systems could improve diagnostic accuracy, personalize interventions, and optimize public health resource allocation, although they stress that challenges like data privacy, algorithmic bias, and validation remain as major hurdles in integrating them fully into clinical systems.
Outcomes and Implications
Cardiovascular disease remains a leading cause of morbidity and mortality, and the long-term impacts of COVID-19 have further strained healthcare systems. COVID-19 infection has also greatly raised the risk of cardiovascular complications, both in individuals with pre-existing heart conditions and in those without them. Post-COVID patients were shown to experience long-term cardiovascular effects such as myocarditis, arrhythmias, and an elevated risk of thromboembolic events, which makes timely detection and monitoring essential. By exploring how AI can be used to enhance detection, prediction, and care management, the study addresses the urgent need for machine learning and innovation in post-pandemic cardiovascular care. Clinically, AI tools could help physicians identify high-risk patients earlier, track their disease progression more effectively, and tailor interventions and treatments to their individual and specific needs. Integration with imaging, lab data, and wearables provides a non-invasive and scalable way to monitor patients outside of hospital settings. AI is being increasingly integrated into the public health sector as a tool for early detection, prediction, and management of disease at the individual and population levels by predicting disease trends, supporting health surveillance, and optimizing resource allocations. AI was shown to enhance risk prediction, medical imaging analysis, drug development, remote monitoring, and population-level insights among cardiovascular patients. Ultimately, these applications could improve health outcomes, reduce healthcare costs, and support more personalized cardiovascular medicine in a post-pandemic world.