Artificial intelligence models enhance heart failure diagnosis, risk assessment, and treatment by analyzing complex data patterns to predict hospital admissions and assist clinical decision-making.
AI technologies offer significant potential to enhance heart failure management across diagnosis, risk prediction, and readmission forecasting.
Recent advancements in artificial intelligence (AI) have revolutionized the diagnosis, risk assessment, and treatment of heart failure (HF). AI models have demonstrated superior performance in distinguishing healthy individuals from those at risk of congestive HF by analyzing heart rate variability data. In addition, AI clinical decision support systems exhibit high concordance rates with HF experts, enhancing diagnostic precision. For HF with reduced as well as preserved ejection fraction, AI-powered algorithms help detect subtle irregularities in electrocardiograms and other related predictors. AI also aids in predicting HF risk in diabetic patients, using complex data patterns to enhance understanding and management. Moreover, AI technologies help forecast HF-related hospital admissions, enabling timely interventions to reduce readmission rates and improve patient outcomes. Continued innovation and research are crucial to address challenges related to data privacy and ethical considerations and ensure responsible implementation in healthcare.
Fareed et al. (2025) conducted a review in Heart failure. Artificial intelligence (AI) was evaluated. Artificial intelligence models enhance heart failure diagnosis, risk assessment, and treatment by analyzing complex data patterns to predict hospital admissions and assist clinical decision-making.