ArticleJournal of cardiovascular electrophysiology2026
Artificial Intelligence-Enhanced Implantable Loop Recorders in Pediatric Patients: Effects on Device Performance and Clinical Workflow in a Single-Center Experience.
Article in Journal of cardiovascular electrophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Smarter Alerts, Smaller Patients: AI-Enhanced Implantable Loop Recorder Monitoring in Children.Journal of cardiovascular electrophysiology · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI)-based algorithms have been developed to reduce alert burden in patients with implantable loop recorders (ILRs), but their performance in pediatric populations has not been previously evaluated.
methodsWe retrospectively analyzed pediatric patients implanted with an AI-enhanced ILR (Linq2, Medtronic) incorporating AI-based filtering (AccuRhythm algorithms). The impact of AI on nonactionable alerts (NAAs) and estimated clinical workflow was assessed using previously published time-and-motion models.
resultsForty-five patients (10.8 ± 4.7 years) were included. AI-based filtering significantly reduced NAAs (43.7%), driven predominantly by a marked reduction in pause-related alerts (75%), corresponding to an estimated saving of 14 clinic hours over 3 months (58 projected hours annually).
conclusionsIn this exploratory pediatric experience, AI-enhanced ILR monitoring substantially reduced the burden of NAAs, particularly for pause alerts. These findings highlight the importance of population-specific AI algorithms and suggest that AI-based filtering may improve the efficiency and sustainability of monitoring in pediatric electrophysiology.
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