Overview: This review discusses the use and future directions of AI in cardiology, focusing on areas like electrocardiography, telemetry and wearables, echocardiography, CMR, nuclear cardiology, CT, electrophysiology studies, coronary angiography, and genetics or multiomics.

AI Glossary: Includes key terms such as algorithms, AUC, artificial intelligence, neural networks, classification, CNNs, deep learning, features, foundation models, joint embedding, labels, large language models, machine learning, preprocessing, reinforcement learning, segmentation, semi-supervised learning, structured data, supervised learning, unstructured data, unsupervised learning, and wearables.

Deep Learning in Cardiology: Applied to physiologic waveform, imaging, and multiomics data with clinical applications. Studies reviewed using MeSH terms in PubMed.

ECG and AI: Deep learning techniques like CNNs show promise in arrhythmia classification and predicting conditions like LV systolic dysfunction, hypertrophic cardiomyopathy, and cardiac amyloidosis.

AI in Echocardiography: Improves image acquisition and interpretation, helping automate measurements and enhancing variability and disease diagnosis.

AI in CMR Imaging: Enhances image reconstruction, segmentation, and quantification. AI applications in nuclear cardiology and CT include improved prognostication and plaque burden quantification.

AI in Electrophysiology: Aids preprocedural planning, intraprocedural guidance, and postprocedural predictions, improving ablation target identification and therapy response prediction.

AI in Coronary Angiography: Automates stenosis detection, plaque characterization, and fractional flow reserve computation, enhancing accuracy and procedural efficiencies.

Machine Learning in Genomics: Improves risk prediction, variant interpretation, pathogenicity identification, and integration into clinical care.

Future of AI in Cardiovascular Medicine: Promises enhanced disease screening, imaging data integration, and accurate diagnoses. Focuses on data quality, diversity, model generalizability, and promoting AI adoption in clinical practice.

AI Potential: Significant potential to enhance patient care through improved diagnostics, risk stratification, and personalized treatment plans, supporting clinicians in delivering better cardiovascular care.