Artificial Intelligence in Heart Failure: From Risk Prediction to Real-Time Decision Support

Heart failure is one of the most prevalent and debilitating cardiovascular conditions, contributing significantly to global morbidity, mortality, and healthcare costs. Despite advances in diagnosis and therapy, patient outcomes remain heterogeneous, reflecting the multifactorial nature of the disease and the challenge of achieving precision care. Recent years have witnessed a surge in the application of artificial intelligence (AI) and machine learning (ML) tools in cardiology, specifically within heart failure research and management. These technologies are being leveraged to interpret complex multimodal data, including imaging, biomarkers, electronic health records, and wearable sensor streams, and to detect patterns that escape conventional analysis.This Research Topic aims to consolidate the most recent advances at the interface between artificial intelligence and heart failure medicine. We seek studies that not only develop predictive and diagnostic algorithms but also translate them into clinically actionable tools capable of improving patient care and outcomes. By bringing together contributions from clinicians, data scientists, and engineers, the Topic intends to explore how AI can transform risk modeling from static prediction to adaptive, real-time guidance. Key questions include how AI-enhanced systems can improve detection of decompensation, streamline therapy optimisation, and support clinicians at the point of care.We welcome contributions addressing, but not limited to: AI and ML models for predicting incident heart failure and risk; ML applied to echocardiography, ECG, imaging, and biomarkers; real-time monitoring and AI-powered decision support; early detection of acute decompensation; AI-assisted titration of guideline-directed therapy; and wearables and digital biomarkers in heart failure. We also encourage work on model validation, generalisability, interpretability, and the equitable and safe implementation of AI in clinical practice. Topic editors: Maria Concetta Pastore, Hatem Soliman-Aboumarie Source: https://www.frontiersin.org/research-topics/84305/artificial-intelligence-in-heart-failure-from-risk-prediction-to-real-time-decision-support