Artificial Intelligence in Cardiovascular Health: Insights into Post-COVID Public Health Challenges

Area of Science:

• Cardiology
• Public Health
• Artificial Intelligence

Background:

• Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality.
• Risk factors like diabetes, hypertension, obesity, and smoking exacerbate CVDs.
• The COVID-19 pandemic revealed a strong link between viral infections and cardiovascular health, with SARS-CoV-2 causing myocardial injury, endothelial dysfunction, and thrombosis.

Purpose of the Study:

• To explore the impact of COVID-19 on cardiovascular health.
• To investigate the role of artificial intelligence (AI) in addressing cardiovascular complications.
• To highlight AI's potential in cardiovascular medicine and public health strategies.

Main Methods:

• Review of current literature on COVID-19 and cardiovascular complications.
• Analysis of artificial intelligence (AI) applications including machine learning (ML) and deep learning (DL).
• Examination of AI's role in risk prediction, biomarker discovery, medical imaging, remote monitoring, and public health surveillance.

Main Results:

• SARS-CoV-2 infection is linked to increased severity of CVD outcomes and persistent complications in Long COVID.
• AI enhances cardiovascular risk prediction, biomarker discovery, and imaging analysis (echocardiography, CT, MRI).
• AI-powered remote monitoring and decision support systems improve diagnosis, personalized treatment, and health equity.

Conclusions:

• Continued research and public health strategies are crucial for mitigating long-term cardiovascular risks post-COVID.
• AI integration in cardiovascular medicine and public health provides data-driven solutions for managing post-COVID complications.
• AI offers efficient, patient-centered approaches to improve cardiovascular health and outcomes.