ArticleCureus2026
Use of an Artificial Intelligence Algorithm to Increase Productivity in Implantable Loop Recorder Monitoring: A Multicentre Observational Study.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundInsertable loop recorder (ILR) services are increasingly constrained by high volumes of transmitted episodes, many of which are false, clinically irrelevant, or non-actionable. Cloud-based artificial intelligence (AI) algorithms have the potential to suppress false atrial fibrillation (AF) and pause alerts while preserving clinically relevant events. We present the first real-world impact of an AI algorithm activated across a fixed multicentre ILR cohort.
methodsWe performed a retrospective, multicentre before-and-after cohort analysis of consecutive patients. Eligible patients had continuous ILR monitoring for 12 months before and 12 months after service-wide activation ("switch-on") of the Medtronic AccuRhythm AI platform (Medtronic plc, Galway, Ireland), enabling within-patient comparison. All clinician-facing transmitted alerts were counted in each period. Secondary analyses examined the concentration of alert burden across patients and estimated workflow impact using published time-and-motion data for remote transmission review. Differences in alert counts were tested using paired t-tests, reporting mean paired differences with 95% confidence intervals (CI). Results: The cohort included 445 patients (Reveal LINQ n=438; LINQ II n=7); 440 (99%) were implanted for syncope (mean age 67±15 years; 49.6% male). Total transmitted alert volume fell from 4,261 pre-AI to 2,509 post-AI (29% reduction). The mean paired within-patient change was -3.94 alerts (95% CI -7.5 to -0.4; p<0.05). Alerts were highly concentrated: 7% of patients generated >90% of alerts, and 104 patients had intermittent disconnection from remote monitoring. Applying established workflow timings (11-13 minutes per remote transmission review) translated the reduction into 185-218 hours of physiologist review time released annually: approximately four hours per week of "virtual physiologist" capacity.
conclusionsIn routine practice, AI activation in a multicentre observational study was associated with a statistically significant reduction in ILR alert burden and a clinically meaningful release of staff capacity. Parallel management of high-alert patients and connectivity optimisation may further amplify the operational benefit.
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.