000165556 001__ 165556
000165556 005__ 20260112132215.0
000165556 0247_ $$2doi$$a10.1109/JBHI.2025.3628501
000165556 0248_ $$2sideral$$a147258
000165556 037__ $$aART-2025-147258
000165556 041__ $$aeng
000165556 100__ $$aRucco, Andrea$$uUniversidad de Zaragoza
000165556 245__ $$aEnhancing Chronic Heart Failure Monitoring, Prevention, and Management with IoT and AI: A Systematic Literature Review
000165556 260__ $$c2025
000165556 5203_ $$aChronic Heart Failure (CHF) represents a significant global health concern due to its high morbidity and mortality rates. Effectively addressing this challenge requires scalable technology solutions to shift Heart Failure (HF) care from episodic reactive treatment to continuous personalized management. As digital health technologies advance, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) into CHF care enables the development of scalable monitoring, prevention, and management strategies and real-time Clinical Decision Support Systems (CDSSs). This Systematic Literature Review (SLR) analyzes 67 peer-reviewed studies published between January 2021 and May 2024, selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to evaluate the technological and clinical impacts of AI-enabled systems in CHF and broader HF care. The review identifies emerging trends, discusses dataset characteristics and clinical relevance, identifies IoT integration patterns, gaps, and deployment barriers, and highlights opportunities for improving the integration of AI/IoT systems into HF care workflows. The studies are organized into four clinical application domains: HF detection, phenotyping and classification, risk stratification, and other miscellaneous applications. Our findings highlight the progress in AI/IoT synergy; however, challenges remain in dataset heterogeneity and coverage, reproducibility, benchmarking practices, and clinical workflow integration, particularly as IoT integration is often limited or insufficiently explored. Our primary recommendations emphasize the use of multimodal datasets, the adoption of interpretable modeling approaches, and stronger interdisciplinary collaboration to improve clinical applicability and support integration into real-world settings.
000165556 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000165556 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165556 700__ $$aTafadzwa Shumba, Ángela
000165556 700__ $$aMontanaro, Teodoro
000165556 700__ $$aSergi, Ilaria
000165556 700__ $$aPatrono, Luigi
000165556 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000165556 773__ $$g(2025), [16 pp.]$$pIEEE j. biomed. health inform.$$tIEEE journal of biomedical and health informatics$$x2168-2194
000165556 8564_ $$s1078701$$uhttps://zaguan.unizar.es/record/165556/files/texto_completo.pdf$$yVersión publicada
000165556 8564_ $$s4111550$$uhttps://zaguan.unizar.es/record/165556/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165556 909CO $$ooai:zaguan.unizar.es:165556$$particulos$$pdriver
000165556 951__ $$a2026-01-12-11:10:01
000165556 980__ $$aARTICLE