Emerging Deep Learning Architectures for Heart Failure Prediction and Cardiovascular Disease Monitoring

Abstract

Decentralized Cardiovascular diseases (CVDs) remain the leading cause of global mortality, accounting for nearly 20 million deaths annually, while heart failure (HF) continues to impose substantial clinical and economic burdens across both developed and emerging healthcare systems. Recent advances in artificial intelligence (AI), particularly deep learning (DL), have transformed predictive cardiology through automated feature extraction, multimodal clinical integration, and real-time physiological monitoring. This narrative review critically synthesizes recent evidence published between 2022 and 2026 regarding emerging DL architectures for HF prediction and cardiovascular monitoring. The review evaluates the clinical utility of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformers, graph neural networks (GNNs), and multimodal fusion systems in electrocardiography (ECG), imaging diagnostics, remote monitoring, and personalized cardiovascular care. Particular attention is given to explainable AI (XAI), federated learning, wearable technologies, and digital twin systems that increasingly support precision cardiology. The review further identifies persistent challenges involving data heterogeneity, limited external validation, algorithmic bias, interpretability limitations, and computational scalability. Evidence indicates that transformer-based and multimodal DL systems now outperform conventional machine learning approaches in several cardiology applications, especially arrhythmia detection, mortality prediction, and HF readmission forecasting. However, clinical deployment remains constrained by regulatory uncertainty, interoperability limitations, and insufficient prospective validation across diverse populations. The review concludes that future cardiovascular AI systems will increasingly rely on explainable, privacy-preserving, and patient-specific architectures capable of integrating imaging, biosignals, genomics, and longitudinal electronic health records (EHRs). Interdisciplinary collaboration among clinicians, AI scientists, biomedical engineers, and regulatory agencies remains essential for translating DL innovations into safe and scalable cardiovascular healthcare solutions.