News | Implantable Cardiac Monitor (ICM) | April 14, 2026

New Study Uncovers Why False Positives Persist in AI-Equipped Implantable Cardiac Monitors

Research identifies guideline-based interpretation gaps and signal-detection issues behind non-actionable alerts and shows how an additional cloud-based AI layer can significantly reduce clinician review burden while maintaining high sensitivity.

cardiac, monitor,

April 14, 2026 — Implicity, a provider of remote patient monitoring and cardiac data management solutions, recently presented new research at the 2026 European Heart Rhythm Association Congress (EHRA) examining why false-positive alerts remain a persistent challenge in implantable cardiac monitors (ICMs) — even in devices equipped with manufacturer AI algorithms. While physicians experience this burden every day, the findings provide new insight into why it persists, identifying guideline-based interpretation gaps and signal-detection issues as key drivers of non-actionable alerts across modern ICM platforms.

In a cross-manufacturer analysis of 2,659 rhythm episodes from 1,710 patients implanted with ICMs from Medtronic, Biotronik, Abbott and Boston Scientific, findings revealed that even in AI-equipped devices, 32.9% of episodes were still non-actionable, with another 30.6% deemed indeterminate. Among devices without proprietary AI algorithms, 45.4% of episodes were non-actionable and 20.1% indeterminate.

To conduct the analysis, an independent expert adjudication committee applied a standardized annotation framework aligned with international electrophysiology guidelines to determine whether device-detected episodes met the diagnostic criteria for clinically meaningful arrhythmias.

The findings provide new insight into why false-positive alerts persist even as device algorithms have evolved. Investigators found that many alerts stem from how device algorithms interpret rhythm signals relative to guideline-defined arrhythmia criteria. When those interpretations diverge from clinical definitions, benign rhythms or signal artifacts, such as premature ventricular contractions or electrical noise, may be labeled as clinically significant events.

The analysis also identified specific signal-detection mechanisms contributing to these alerts. Episodes labeled as cardiac “pause” events emerged as a major driver, with 46.8% ultimately determined to be false positives caused by R-wave undersensing, where the device fails to detect a heartbeat and incorrectly interprets the signal as a pause.

“False-positive alerts remain one of the biggest operational challenges in remote cardiac monitoring,” said Niraj Varma, MD, PhD, Professor of Medicine and Consultant Electrophysiologist at the Cleveland Clinic. “Every episode flagged by an implantable cardiac monitor must be reviewed by a clinician, yet even devices equipped with manufacturer AI algorithms still generate a substantial number of non-actionable alerts. When interpretation varies across device platforms and guideline definitions are not consistently applied, it becomes more difficult for physicians to quickly determine which events truly require clinical attention.”

Building on these findings, investigators conducted a second analysis, also presented at EHRA, to examine whether an additional AI layer could help address these persistent false-positive alerts. The study evaluated the Implicity ILR ECG Analyzer*, a cloud-based algorithm designed to analyze ICM transmissions across multiple manufacturer platforms using a standardized guideline-based framework.

The results showed that Implicity’s cloud-based AI algorithm maintained very high sensitivity for detecting clinically meaningful arrhythmias — 98.3% in AI-equipped devices and 94.3% in non-AI models—while filtering a substantial proportion of non-actionable alerts. Specificity reached 61.6% and 75.6% respectively, with a consistent positive predictive value of approximately 74% across both groups, demonstrating reliable diagnostic performance across different generations of implantable cardiac monitors.

“Remote monitoring only works if clinicians can trust the alerts they receive,” said Arnaud Rosier, MD, PhD, electrophysiologist, CEO, and co-founder of Implicity. “When a large share of those alerts are non-actionable, the burden is not just operational—it diverts valuable clinical time from patients who may truly need attention. Our data shows that adding a standardized, guideline-based AI layer can reduce that noise while maintaining the high sensitivity needed to detect clinically meaningful arrhythmias.”

The research presented at EHRA is part of Implicity’s broader clinical program focused on improving the accuracy and efficiency of remote cardiac monitoring.  

 

* The version of ILR ECG Analyzer evaluated in this study (V2) is not yet FDA cleared. Results may not be directly applicable to the currently cleared version


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