The article "Artificial Intelligence in Heart Failure: Friend or Foe?" by Angeliki Bourazana et al. provides an overview of the current applications and challenges of artificial intelligence (AI) in heart failure (HF) management. The authors highlight the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. They discuss the discrepancies between existing models in heart failure diagnostic algorithms and the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment. The review also addresses the challenges in implementing AI, including variable considerations and biases in training data, and the limitations of current AI models in real-world scenarios. The authors suggest that AI can be a valuable tool for clinicians in treating HF patients, provided that existing medical inaccuracies are addressed before integrating AI into clinical frameworks. The article covers machine learning (ML) techniques such as supervised, unsupervised, and reinforcement learning, and their applications in HF diagnosis, monitoring, and management. It also discusses the use of AI in imaging, particularly in echocardiography and cardiac magnetic resonance (CMR), and the ethical considerations surrounding AI in healthcare. The authors conclude that while ML and AI have the potential to benefit patients and cardiologists, their full potential can only be realized if clinicians actively participate in their integration and utilization.The article "Artificial Intelligence in Heart Failure: Friend or Foe?" by Angeliki Bourazana et al. provides an overview of the current applications and challenges of artificial intelligence (AI) in heart failure (HF) management. The authors highlight the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. They discuss the discrepancies between existing models in heart failure diagnostic algorithms and the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment. The review also addresses the challenges in implementing AI, including variable considerations and biases in training data, and the limitations of current AI models in real-world scenarios. The authors suggest that AI can be a valuable tool for clinicians in treating HF patients, provided that existing medical inaccuracies are addressed before integrating AI into clinical frameworks. The article covers machine learning (ML) techniques such as supervised, unsupervised, and reinforcement learning, and their applications in HF diagnosis, monitoring, and management. It also discusses the use of AI in imaging, particularly in echocardiography and cardiac magnetic resonance (CMR), and the ethical considerations surrounding AI in healthcare. The authors conclude that while ML and AI have the potential to benefit patients and cardiologists, their full potential can only be realized if clinicians actively participate in their integration and utilization.