
Truveta
Saving lives with data by providing regulatory-grade safety and effectiveness data.

Accelerating heart disease diagnosis with cloud-native AI-powered ECG interpretation.

Cardiologs, a subsidiary of Philips, is a pioneering cloud-native platform that leverages Deep Learning to transform ECG analysis. Built on a foundation of millions of proprietary clinical records, the platform's neural network architecture is designed to identify over 20 distinct cardiac arrhythmias with clinical-grade precision. Positioned as a market leader in 2026, Cardiologs operates as a vendor-agnostic solution, allowing healthcare providers to ingest data from any digital ECG device, including Holters, smartwatches, and patches. Its technical edge lies in its ability to significantly reduce the 'noise' of false positives often found in traditional algorithmic ECG software, thereby cutting down manual review time by up to 90%. The platform's 2026 roadmap focuses on predictive analytics for heart failure and stroke risk, moving beyond simple detection into preventative care. By streamlining the triage process, Cardiologs enables healthcare systems to handle the surge in remote patient monitoring data without increasing staff burnout. It is fully integrated into modern clinical workflows via HL7/FHIR standards, making it a critical component of the digital cardiology suite.
Cardiologs, a subsidiary of Philips, is a pioneering cloud-native platform that leverages Deep Learning to transform ECG analysis.
Explore all tools that specialize in arrhythmia detection. This domain focus ensures Cardiologs delivers optimized results for this specific requirement.
Uses Convolutional Neural Networks (CNN) trained on over 20 million clinical ECG signals to detect patterns invisible to the human eye.
Cloud-native platform capable of parsing legacy and modern file formats from any ECG hardware manufacturer.
Instantly flags urgent abnormalities in a queue-based system.
A zero-footprint DICOM-compliant viewer for analyzing high-resolution waveforms in any browser.
Synchronized editing across multiple beats; updating one classification propagates changes where the AI detects similar morphology.
Aggregates historical ECG data to show arrhythmia trends over months or years.
Bidirectional data exchange with EMR systems like Epic and Cerner.
Institutional security assessment and HIPAA/GDPR data processing agreement signing.
Integration of existing ECG device fleet via secure cloud upload protocols (HTTPS/TLS 1.3).
Configuration of HL7/FHIR interfaces for seamless EHR data synchronization.
Creation of clinician user profiles with Role-Based Access Control (RBAC).
Initial AI model calibration for site-specific device variations.
Training of technician staff on the 'Scan-and-Upload' workflow.
Training of cardiologists on the cloud-based web viewer and annotation tools.
Implementation of quality control checks for automated report generation.
Pilot phase execution with a subset of Holter recordings.
Full clinical deployment across the hospital department or health system.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its exceptional time-saving capabilities and high accuracy in AFib detection, though some note the cost is significant for smaller practices."
Post questions, share tips, and help other users.

Saving lives with data by providing regulatory-grade safety and effectiveness data.

Breast AI trusted for better workflow and higher confidence in mammography screening.

Assistive communication solutions for people with disabilities.

Turn your diabetes data points into accessible, actionable, and meaningful insights.

Science-backed supplements for personalized wellness.

Hear everything in a heartbeat with advanced digital stethoscope technology.