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Enhancing Chronic Heart Failure Monitoring, Prevention, and Management With IoT and AI: A Systematic Literature
IEEE Journal of Biomedical and Health Informatics
|November 3, 2025
Summary
Artificial Intelligence (AI) and Internet of Things (IoT) integration shows promise for managing Chronic Heart Failure (CHF). However, challenges in data, reproducibility, and workflow integration need addressing for real-world application.
Area of Science:
- Digital Health
- Biomedical Informatics
- Artificial Intelligence in Healthcare
Background:
- Chronic Heart Failure (CHF) poses a significant global health burden, necessitating advanced technological solutions for continuous, personalized patient management.
- Digital health technologies, particularly Artificial Intelligence (AI) and the Internet of Things (IoT), offer scalable strategies for monitoring, prevention, and real-time clinical decision support in Heart Failure (HF) care.
Purpose of the Study:
- To systematically review and evaluate the technological and clinical impacts of AI-enabled systems in Chronic Heart Failure (CHF) and broader Heart Failure (HF) care.
- To identify emerging trends, analyze dataset characteristics, explore IoT integration, and pinpoint barriers and opportunities for AI/IoT systems in HF management.
Main Methods:
- A Systematic Literature Review (SLR) adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.
- Analysis of 67 peer-reviewed studies published between January 2021 and May 2024, focusing on AI/IoT applications in HF care.
Main Results:
- AI and IoT synergy demonstrates progress in HF detection, phenotyping, classification, and risk stratification.
- Identified challenges include dataset heterogeneity, lack of reproducibility, inconsistent benchmarking, and limited exploration of IoT integration in clinical workflows.
Conclusions:
- While AI/IoT integration in HF care is advancing, significant hurdles in data quality, model interpretability, and seamless clinical workflow integration persist.
- Recommendations include utilizing multimodal datasets, adopting interpretable AI models, and fostering interdisciplinary collaboration to enhance clinical applicability and real-world deployment.
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