Use of Bioimpedance Monitoring and Artificial Intelligence in Heart Failure: A Scoping Review

Heart failure (HF) is frequently accompanied by congestion and fluid accumulation, which are key drivers of decompensation and hospitalization. Bioimpedance wearable sensors (BI), coupled with artificial intelligence (AI) and clinical decision-support systems (CDSS), offer an emerging opportunity to move HF volume-status assessment beyond conventional clinical monitoring towards earlier risk detection and personalized decision support. This scoping review mapped the available evidence on these integrated approaches. Following PRISMA-ScR guidance, systematic searches were conducted in Scopus, IEEE Xplore, PubMed, and Embase, with the final search performed on August 2, 2026. Ten studies were included, of which six applied AI techniques, two incorporated a CDSS, and two included both AI and CDSS. BI measurements were acquired using heterogeneous approaches, including implanted devices, wearables, and other non-invasive systems, and they encompassed intrathoracic impedance, thoracic BI, or whole-body bioimpedance analysis. AI applications focused on HF decompensation, fluid accumulation, pleural effusion, and the risks of hospitalization or readmission, whereas CDSS primarily provided risk information, alerts, or structured clinical decision support. Overall, the integration of BI sensors with AI or CDSS represents a rapidly developing transformative approach to HF volume-status management, with the potential to enable earlier intervention and more individualized clinical decision-making.