ARTIFICIAL INTELLIGENCE IN PERSONALIZED CARDIOLOGY: CURRENT APPLICATIONS, CLINICAL IMPACT, AND FUTURE DIRECTIONS: A REVIEW

Artificial Intelligence has enabled data-driven diagnostic, prognostic, and therapeutic applications in cardiovascular medicine. Contemporary machine learning algorithms improve the sensitivity and specificity of ECG interpretation, echocardiography quantification, and coronary imaging by automating feature extraction and reducing inter-observer variability. Deep neural networks, which are trained on multimodal datasets involving clinical records, biomarkers, genomics, and wearable sensor data, are able to predict major adverse cardiac events with much higher precision compared to traditional risk models. AI-enabled decision support systems allow personalized treatment strategies for heart failure, coronary artery disease, and arrhythmias by projecting an individual response to pharmacological or interventional therapies. Integration with robotic-assisted procedures and electrophysiology mapping enhances procedural precision. Limitations persist with limited dataset diversity, regulatory hurdles, model interpretability, and the need for prospective clinical validation. Notwithstanding such limitations, AI is a very important catalyst in the evolution of precision cardiology.