Artificial Intelligence in Heart Failure: Friend or Foe? This review explores the current applications and challenges of artificial intelligence (AI) in heart failure (HF) management. It highlights the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in HF diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. The review also addresses challenges in implementing AI, including variable considerations and biases in training data. It emphasizes the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating HF patients, as long as existing medical inaccuracies have been addressed before integrating AI into these frameworks. AI in HF includes applications in diagnosis, monitoring, and treatment. Machine learning (ML) techniques such as supervised, unsupervised, and reinforcement learning are discussed, along with deep learning and convolutional neural networks (CNNs). AI has shown promise in improving HF diagnosis, particularly through the use of ECG, echocardiography, and chest X-rays. AI-based monitoring with sensors has the potential to enhance results by providing real-time data for early detection of HF decompensation. ML-based prediction of response to cardiac resynchronization therapy (CRT) is also explored, with studies showing that AI can help identify patients likely to benefit from CRT implantation. AI is also being used in imaging HF, including image segmentation and echocardiography. AI-based echocardiography can improve the accuracy and efficiency of cardiac function assessment. Cardiac MRI and nuclear cardiology are also areas where AI is being applied to enhance diagnostic accuracy and risk prediction. However, the application of AI in HF faces challenges, including the potential for systematic errors due to biased data, the need for accurate clinical diagnosis, and the difficulty in interpreting AI predictions. Ethical considerations, such as systemic biases and the need for equitable representation in AI algorithms, are also discussed. The review concludes that while AI has the potential to bring significant benefits to both patients and cardiologists, its potential can only be fully realized if clinicians actively participate in the integration and utilization of these innovative algorithms. The application of AI in HF remains constrained by ongoing challenges, including the need for high-quality training data and the difficulty in interpreting AI predictions. Despite these challenges, AI has the potential to revolutionize the management of HF by providing more accurate and efficient diagnostic and treatment options.Artificial Intelligence in Heart Failure: Friend or Foe? This review explores the current applications and challenges of artificial intelligence (AI) in heart failure (HF) management. It highlights the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in HF diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. The review also addresses challenges in implementing AI, including variable considerations and biases in training data. It emphasizes the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating HF patients, as long as existing medical inaccuracies have been addressed before integrating AI into these frameworks. AI in HF includes applications in diagnosis, monitoring, and treatment. Machine learning (ML) techniques such as supervised, unsupervised, and reinforcement learning are discussed, along with deep learning and convolutional neural networks (CNNs). AI has shown promise in improving HF diagnosis, particularly through the use of ECG, echocardiography, and chest X-rays. AI-based monitoring with sensors has the potential to enhance results by providing real-time data for early detection of HF decompensation. ML-based prediction of response to cardiac resynchronization therapy (CRT) is also explored, with studies showing that AI can help identify patients likely to benefit from CRT implantation. AI is also being used in imaging HF, including image segmentation and echocardiography. AI-based echocardiography can improve the accuracy and efficiency of cardiac function assessment. Cardiac MRI and nuclear cardiology are also areas where AI is being applied to enhance diagnostic accuracy and risk prediction. However, the application of AI in HF faces challenges, including the potential for systematic errors due to biased data, the need for accurate clinical diagnosis, and the difficulty in interpreting AI predictions. Ethical considerations, such as systemic biases and the need for equitable representation in AI algorithms, are also discussed. The review concludes that while AI has the potential to bring significant benefits to both patients and cardiologists, its potential can only be fully realized if clinicians actively participate in the integration and utilization of these innovative algorithms. The application of AI in HF remains constrained by ongoing challenges, including the need for high-quality training data and the difficulty in interpreting AI predictions. Despite these challenges, AI has the potential to revolutionize the management of HF by providing more accurate and efficient diagnostic and treatment options.