Enhancing Chronic Heart Failure Monitoring, Prevention, and Management with IoT and AI: A Systematic Literature Review

Abstract

Chronic Heart Failure (CHF) represents a significant global health concern due to its high morbidity and mortality rates. Effectively addressing this challenge requires scalable technology solutions to shift Heart Failure (HF) care from episodic reactive treatment to continuous personalized management. As digital health technologies advance, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) into CHF care enables the development of scalable monitoring, prevention, and management strategies and real-time Clinical Decision Support Systems (CDSSs). This Systematic Literature Review (SLR) analyzes 67 peer-reviewed studies published between January 2021 and May 2024, selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to evaluate the technological and clinical impacts of AI-enabled systems in CHF and broader HF care. The review identifies emerging trends, discusses dataset characteristics and clinical relevance, identifies IoT integration patterns, gaps, and deployment barriers, and highlights opportunities for improving the integration of AI/IoT systems into HF care workflows. The studies are organized into four clinical application domains: HF detection, phenotyping and classification, risk stratification, and other miscellaneous applications. Our findings highlight the progress in AI/IoT synergy; however, challenges remain in dataset heterogeneity and coverage, reproducibility, benchmarking practices, and clinical workflow integration, particularly as IoT integration is often limited or insufficiently explored. Our primary recommendations emphasize the use of multimodal datasets, the adoption of interpretable modeling approaches, and stronger interdisciplinary collaboration to improve clinical applicability and support integration into real-world settings.

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