ReviewPacing and clinical electrophysiology : PACE2025
From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.
Review in Pacing and clinical electrophysiology : PACE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A Multi-Center Trained Residual Neural Network for Robust Classification of Atrial High-Rate Episodes in Remotely Monitored Pacemakers and Defibrillators.Sensors (Basel, Switzerland) · 2026Article
- AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes.Sensors (Basel, Switzerland) · 2026Review
- Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy.Journal of clinical medicine · 2025Review
- From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.Pacing and clinical electrophysiology : PACE · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of telehealth, particularly remote monitoring (RM), has profoundly improved the care of patients with cardiac implantable electronic devices (CIEDs). The recent COVID-19 pandemic has further accelerated the adoption of RM systems. The implementation of RM to standard clinical care has been accompanied by a surge of device transmissions. Especially unscheduled transmissions have resulted in an overwhelming workload for clinicians. As the number of device transmissions is expected to increase further while clinical resources remain limited, workflow optimization is crucial. Artificial intelligence (AI) presents a promising solution. This review outlines recent advances in RM and AI applications for CIEDs. It explores the potential of AI to streamline RM workflows, reduce clinician workload, and enhance heart failure care by enabling early detection of clinical deterioration and timely intervention. In addition, key barriers to implementation are addressed, including data standardization and regulatory considerations. Beyond improving monitoring efficiency and patient outcomes, AI-supported RM may also help expand access to care through more effective resource allocation and contribute to a more sustainable, future-proof healthcare system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.