AI Tools for Heart Failure Management: A Comprehensive Review of Potential, Pitfalls, and Predictive Analytics

Heart failure (HF), as a sequela of cardiac insult, has long been recognized for the excessive burden it places on healthcare systems worldwide. Advancements have been made in both the interventional and pharmacological landscapes related to the disease, with monumental strides achieved in reducing morbidity and mortality. However, patients continue to live in fear of the disease as they face the risk of repeated hospitalizations, adverse outcomes, and the financial strain it imposes. Despite the vast amount of literature available to clinicians, bridging the gap between theoretical knowledge and clinical practice remains challenging due to persistent knowledge gaps. Integrating clinical data, identifying patterns in key investigations, and making informed clinical decisions are difficult, particularly when tailoring treatments to each patient's unique characteristics. AI has shown great potential in addressing these challenges and assisting clinicians. Through this review, we aim to demonstrate how AI algorithms and models, such as machine learning, deep learning, and natural language processing, can support various aspects of HF management. This narrative review was conducted through a comprehensive and structured literature search on PubMed. Screening identified 163 articles that met the inclusion criteria from an initial total of 1,617. Data extraction included author name, study type, digital object identifier, study objective, sample size, key findings, and relevance to AI applications in HF management. Recent literature on AI and HF highlights the significant impact of AI on expanding the scope of practice in this field. Several key findings stand out: (1) AI has enhanced the detection of subclinical HF (i.e., the presence of HF without noticeable symptoms); (2) AI algorithms, when compared to traditional methods, demonstrate greater accuracy in identifying the most suitable treatment for HF according to patient characteristics; and (3) human-machine collaborative models have proven superior in predicting one-year readmission rates for patients with HF. Several challenges, such as algorithmic bias, data security concerns, the "black box" nature of AI, and other risks of bias, have also been identified. Nevertheless, with ethical oversight and regular clinical engagement, AI continues to demonstrate significant potential in HF management. With the latest advances, AI is poised to play an even greater role in transforming HF care, shifting it toward more proactive and data-driven models.