Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring

Abstract

Introduction and purpose: Artificial intelligence (AI) and machine learning (ML) are impacting cardiology by enhancing diagnostic accuracy, personalizing treatment and optimizing patient care. This review examines current and emerging applications of AI and ML in cardiology, highlighting their transformative impact on clinical practice, workflow efficiency, and long-term patient outcomes.

Description of the state of knowledge: AI and ML, including advanced neural networks and predictive analytics, demonstrate exceptional sensitivity and specificity in interpreting electrocardiograms (ECGs), echocardiograms, CT scans, and cardiac MRIs. These technologies facilitate early detection of conditions such as coronary artery disease, atrial fibrillation, and hypertrophic cardiomyopathy, while also enabling risk stratification for heart failure, myocardial infarction, and sudden cardiac death. Additionally, AI-driven algorithms support personalized treatment strategies, real-time remote monitoring, and precision-guided coronary interventions, reducing procedural complications. Recent advancements also show promise in automating echocardiographic measurements and optimizing cardiac resynchronization therapy, further enhancing diagnostic and therapeutic precision.

Conclusions: AI and ML hold transformative potential for cardiology, enabling faster, more accurate diagnoses and data-driven therapeutic decisions. Their integration into clinical practice promises to improve prognostic accuracy, reduce healthcare costs, and enhance patient-centered care.