Study Shows Implicity’s New Agnostic Cloud-Based AI Algorithm Further Reduces False Alerts Even After Manufacturer AI Filtering in Modern Devices New HRS data extends EHRA findings with a specific analysis of next-gen ILRs, demonstrating that, on top of manufacturer-embedded AI, Implicity filters out 61.6% of false positives while maintaining 98.3% sensitivity  Cambridge, Mass., April 25, 2026 –  Implicity, a leader in remote patient monitoring and cardiac data management solutions, today announced the latest findings presented at the Heart Rhythm Society (HRS) 2026.  The new HRS data extends on research presented earlier this month at the European Heart Rhythm Association (EHRA) Congress, which showed why false-positive alerts persist even in modern implantable loop recorders (ILRs). The follow-on HRS study specifically focused on the latest AI-equipped ILRs, examining whether layering Implicity’s new, universal cloud-based AI algorithm on top of proprietary manufacturer algorithms could further reduce false alerts. In a review of 483 cases from 324 patients using AI-equipped devices, Implicity’s next-generation ILR ECG Analyzer successfully reclassified 61.6% of false-positive alerts in interpretable episodes while maintaining 98.3% sensitivity, helping reduce the workload for clinicians without compromising patient safety. “Manufacturer filtering has clearly advanced, whether it happens in the device, in the cloud, or through a combination of both. But even in these newer systems, residual false-positive alerts still remain and continue to burden clinical teams. The findings from our HRS study demonstrate  that a manufacturer-agnostic analysis layer can substantially reduce that remaining noise while maintaining the high sensitivity needed to protect patient safety,” said Arnaud Rosier, MD, PhD, electrophysiologist, CEO, and co-founder of Implicity. Implicity’s next-generation ILR ECG Analyzer is designed to act as an agnostic second-layer for residual alerts that remain after the initial manufacturer filters. Operating in the cloud and compatible with all major brands, Implicity’s new universal algorithm applies the most sophisticated neural networks, drastically reducing false positives without compromising patient safety.  This ‘dual AI‘ approach does more than just streamline remote cardiac monitoring workflows; it also reduces alert fatigue by providing clinical teams with highly accurate, trustworthy data. With greater confidence in the data, clinicians can spend less time on noise and more time focusing on meaningful cardiac events. “Newer AI-enabled ILRs do provide a cleaner data stream with fewer obvious artifacts, but they are not perfect, and the false positives that remain are often more subtle and clinically ambiguous,” said Niraj Varma, MD, PhD, Professor of Medicine and Consultant Electrophysiologist at the Cleveland Clinic. “Those are exactly the alerts that still require careful adjudication. In a remote monitoring workflow, that matters enormously, and any technology that can safely mitigate ILR-related false-positive alerts is significant.” These findings follow evidence presented at EHRA examining why false-positive alerts persist across modern ICM devices. In that work, investigators identified guideline-based interpretation gaps and signal-detection issues as key drivers of non-actionable alerts and showed that even in AI-equipped devices, 32.9% of episodes remained non-actionable, with another 30.6% deemed indeterminate. Together, the EHRA and HRS analyses highlight both the mechanisms behind persistent false positives and the potential for a universal, , cloud-based AI approach to help address them across all devices—including those with or without proprietary AI. .   Implicity’s next-generation ILR ECG Analyzer Algorithm (V2) is not FDA cleared yet. About ImplicityImplicity is a digital MedTech software company dedicated to providing the best remote care to patients with cardiac implantable electronic devices and heart failure. Co-founded by cardiac electrophysiologist Arnaud Rosier, MD, PhD, the platform aggregates, normalizes, and standardizes data from any implantable cardiac device across all manufacturers, improving care for patients with cardiac implants and heart failure. Implicity’s platform provides critical health information augmented by FDA-cleared AI** algorithms, enabling healthcare providers to make more informed decisions for better patient outcomes while optimizing workflows. With access to the Health Data Hub—a gateway to one of the world’s largest databases of heart disease patients encompassing nearly the entire French cardiac patient population—Implicity is able to develop its AI solutions based on more robust data. The company is protecting more than 110,000 patients in over 250 medical facilities across the US and Europe. To learn more, visit www.implicity.com or schedule a meeting with the Implicity team. Contact Daniel Martin, Vice President for US Sales, at daniel.martin@implicity.com or visit Booth #1819 at HRS 2026. Health Data Hub is a health data platform established by the French government to combine existing health patient databases and facilitate their usage for research and development purposes * 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 ** SignalHF is an FDA-cleared Class II medical device, see the instructions for use for more information. IM007 ILR ECG Analyzer is an FDA cleared Class II medical device and CE marked Class I (under MDD) medical device, see the instructions for use for more information. IMPLICITY®, Inc. a Delaware corporation with offices located at 185 Alewife Brook Pkwy 210, Cambridge, MA 02138 USA, file number 5917973. Media Contact: Samantha Choinski eMedia Junction  samantha@emediajunction.com ¹IM009: 2021. Manufacturer: Implicity. IM009 is a software as a medical device (SaMD) intended to be used as an adjunct of a remote monitoring platform to follow-up target population patients. IM009 is compatible with devices with remote monitoring feature such as cardiac implantable electronic devices and connected weight scales. The three main intents of IM009 are to (1) label observations generated by medical devices according to predefined categories, (2) create clinically relevant observations for worsening atrial fibrillation and/or rapid weight gain in the context of heart failure, based on data recorded by the device and (3) label Atrial Fibrillation burden observations generated by cardiac implantable electronic device (CIED) as to be hidden or relevant for health care provider based on patient’s anticoagulation status. Hence, IM009 is designed to reduce the workload burden of healthcare providers/professionals in charge of reviewing the observations received from the patients’ devices. IM009 is not intended for use in life supporting or sustaining systems or Alarm devices and as a