University of Lynchburg Doctoral Project Assignment Repository
Specialty
Cardiology
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in heart failure management, shifting clinical practice from reactive care to proactive, patient-centered intervention. This narrative review synthesizes current evidence and evaluates the integration of artificial intelligence (AI) into heart failure management, focusing on its efficacy in detecting early clinical decompensation and improving patient outcomes. A systematic search of peer-reviewed literature from the past five years was conducted via PubMed and Google Scholar. Evidence indicates that AI has evolved from rule-based systems to deep learning models capable of complex data analysis, including ECGs and electronic health records. AI-mediated interventions demonstrate significant clinical utility, including reduced 90-day all-cause mortality and increased detection of new low-ejection-fraction diagnoses. Furthermore, remote patient monitoring and congestion-guided management have shown promise in lowering hospitalization rates and enhancing patient quality of life. Despite these advancements, significant barriers to widespread clinical implementation remain, including data quality, the “digital divide,” and a lack of large-scale, prospective randomized controlled trials to validate non-invasive wearables. To maximize the clinical impact of AI, future implementation must address these regulatory, ethical, and technical challenges while utilizing human-centered design to ensure equitable accessibility.
Recommended Citation
Zwijacz LM. The Role of AI-Mediated Monitoring in Heart Failure: A Narrative Review. University of Lynchburg Doctoral Project Assignment Repository. 2026; 8(2).
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