Cardiovascular disease (CVD) continues to be a prime leading cause of morbidity and mortality worldwide, demanding proper early diagnosis by means of enhanced detection modalities to curtail poor outcome scenarios. In a blazing fast-paced change, the rapid development of artificial intelligence (AI) has led to drastic changes in the CVD landscape through machine learning, deep learning, wearable technology, and federated learning. An array of support vector machines (SVM) and neural networks to predict the risk is being trained on electronic health records (EHR) and developed on an affordable platform of machine learning models. These approaches aim at incrementally enhancing the analysis of cardiovascular imaging by convolutional neural networks (CNN), utilizing automated interpretation of echocardiograms, MRI, and CT scans. AI-integrated devices, such as smart-watches and ECG patches, allow the patients to undergo continual monitoring of their heart rhythms, helping ensure early intervention for arrhythmias and other abnormalities of the heart. Federated learning is privacy-preserving; it allows for the training of an AI algorithm from multiple institutions without ever exposing sensitive patient data to investigators.

The review at hand discusses the merits, limitations, and usages in practical settings of these AI approaches, and compares them on key metrics: these are accuracy, sensitivity, specificity, and privacy protection. In one way or another, these datasets-HF datasets, MIMIC-III datasets, Cardiac MRI datasets from Stanford, and PhysioNet MIT-BIH-arrhythmia database-have played a key role in training or validating AI models on CVD detection. While the presidential aims are there, impediments remain-regulatory issues, model interpretability, computational complexity, data diversity, and so on. The prognosis for the future research direction should be building multi-AI techniques under one unified framework, with consideration for privacy and clinical validation toward advancing the accuracy, efficacy, and availability of CVD diagnosis and monitoring. By understanding and humbling those challenges, AI can bring to rock the cardiovascular healthcare arena towards personalized treatment approaches and enhanced patient outcomes.

Keywords: Artificial Intelligence, Cardiovascular Disease, Detection, Machine Learning, Deep Learning, and Applications.