Digitally supported interprofessional interaction in healthcare—a scoping review

BackgroundThe increasing complexity of patient care and workforce shortages in healthcare systems necessitate improved interprofessional interaction. Digital technologies offer promising solutions to facilitate such interaction

DP-CARE: a differentially private classifier for mental health analysis in social media posts

IntroductionMental health NLP models are increasingly used to detect psychological states such as stress and depression from user-generated social media content. Although transformer based models

Cognitive frontiers: neurotechnology and global internet governance

This article explores the largely uncharted intersection of neurotechnology and Internet governance on the international policy agenda. Neurotechnologies encompass a broad spectrum of functions and

Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography

IntroductionThe application of deep learning methods in automatic delineation of fiducial points in seismocardiography (SCG) on a beat-to-beat basis provides the possibility of obtaining a

Editorial: Implementing digital twins in healthcare: pathways to person-centric solutions

Over the past decade, digital twins (DTs) have evolved from an engineering metaphor into a powerful paradigm for healthcare innovation. By dynamically linking physical and

Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study

December 13, 2025

IntroductionChronic heart failure (CHF) is associated with frequent hospitalizations, poor quality of life, and high healthcare costs. Despite therapeutic progress, early recognition of clinical deterioration remains difficult. The SMART-CARE study investigates whether artificial intelligence (AI)-enabled remote monitoring using CE-certified wearable devices can reduce hospital admissions and improve patient outcomes in CHF.MethodsSMART-CARE is a prospective, multicenter, observational cohort study enrolling 300 adult patients with CHF (HFrEF, HFmrEF, or HFpEF) across three Italian tertiary centers. Participants are assigned to an intervention group, using wrist-worn, chest-worn, and multiparametric CE-certified wearable devices for six months, or to a control group receiving standard CHF care. Physiological data (e.g., SpO₂, HRV, respiratory rate, skin temperature, sleep metrics) are continuously collected and analyzed in real time through AI algorithms to generate alerts for early clinical intervention. The primary endpoint is a ≥20% reduction in hospital admissions over six months. Secondary outcomes include changes in quality of life (Kansas City Cardiomyopathy Questionnaire), biomarkers (BNP, NT-proBNP, renal function, electrolytes), echocardiographic indices (LVEF, LV volumes), and safety events.ResultsWe hypothesize that AI-driven remote monitoring will significantly reduce hospitalizations, improve quality of life, and favorably impact biochemical and echocardiographic parameters compared to standard care.ConclusionSMART-CARE is designed to evaluate the clinical utility of multimodal wearable devices integrated with AI algorithms in CHF management. If successful, this approach may transform traditional care by enabling earlier detection of decompensation, optimizing resource utilization, and supporting the scalability of remote monitoring in chronic disease management.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT06909682.

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