AI-assisted heart failure management: A review of clinical applications, case studies, and future directions

Abstract

Heart failure is a major global health problem that affects over 64 million people and has significant economic costs. Early diagnosis and effective treatment are crucial, but traditional methods can be limited by the complexity and variability of symptoms. New approaches are needed to improve diagnosis and treatment, such as innovative biomarkers, advanced imaging, and personalized therapy. This study explores the application of artificial intelligence (AI) in heart failure diagnosis. The integration of AI in heart failure care holds transformative potential by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatment plans through sophisticated algorithms and machine learning models. Technologies such as automated image analysis, natural language processing, and wearable devices enable continuous monitoring and timely interventions, improving patient outcomes and reducing hospital readmissions. Despite data privacy and algorithm transparency challenges, AI's ability to process vast datasets and provide real-time insights represents a significant leap forward in heart failure management. This review emphasizes AI's promising applications and future directions in reshaping heart failure care.