Artificial intelligence (AI) is significantly improving the early detection of heart diseases, utilizing everyday tests like electrocardiograms (ECGs) and echocardiograms. AI, especially through deep learning neural networks, allows for quick and accurate interpretation of ECGs, identifying patterns often missed by human analysts.
For instance, AI can detect conditions such as left ventricular dysfunction, silent atrial fibrillation, and acute rejection after heart transplants. Wearable devices with AI capabilities continuously monitor heart signals, enabling early detection of arrhythmias and heart failure decompensation.
In medical imaging, AI enhances image quality and automates the detection of cardiovascular risk markers, such as coronary artery calcium, which can predict cardiovascular events even in asymptomatic populations.
This application of AI in cardiology enables personalized treatment strategies. Predictive models can integrate clinical, laboratory, and genetic variables to tailor therapies, optimizing drug dosages for better clinical outcomes.
AI also evaluates responses to antiplatelet therapies in coronary artery disease and guides stent applications. Remote monitoring through wearable devices helps track changes in heart rate and blood pressure, enhancing treatment adherence and alerting patients to potential hospitalization risks.
However, the implementation of AI in cardiology raises ethical considerations, including patient data privacy, security, and the potential for healthcare inequality. There are also concerns regarding accountability for AI system failures and informed consent, as patients may not fully understand how their data will be used.
Despite these challenges, AI's role in cardiology continues to expand, with recent studies highlighting its capabilities in ECG analysis and cardiac imaging, although further validation in real clinical environments is necessary.