Anumana Appoints Kevin Ballinger and Jean-Luc Butel to Board of Directors

Anumana empowers clinicians to act on the heart's hidden messages. Our AI unlocks clinical insights from every heartbeat to identify at-risk patients earlier and inform ongoing care.

Anumana’s technology platform is built on more than 22 million patient records spanning more than 20 years through exclusive partnerships with leading academic medical centers.1 Our ECG-AI™ analyzes the heart’s electrical signals to reveal insights beyond human detection capabilities. Anumana’s algorithms provide clinically validated insights that help enable earlier intervention and improved patient outcomes.

Contact us to discuss implementation and how ECG-AI can benefit your practice and patients.

Anumana's ECG-AI™ LEF Differentiators1,2

Scientific Depth

Clinically validated, model developed using ≈ 2.9 million ECG-echo pairs from 676,000+ patients across diverse populations.

Portfolio Breadth

Anumana's flagship algorithm detects low ejection fraction (LEF) and is available for clinical use in the US and EU. Additionally, we have a portfolio of cardiac-focused algorithms in development.

Clinical Utility

Seamless EHR integration, works with many ECG systems commonly used in clinical practice, fits into current workflow, eligible for reimbursement today.

 How It Works: ECG-AI in Action

Anumana's AI engine analyzes 60,000 ECG data points, identifies cardiac disease signals invisible to the human eye, and delivers binary findings to support ongoing care.

Anumana AI Can Provide Life-Changing Outcomes

SCIENTIFIC STUDIES

Rigorously Evaluated. Continuously Advancing.

The science behind Anumana’s portfolio of algorithms has been developed and evaluated in more than 100 peer-reviewed publications and presentations. Our pipeline continues to expand - from early disease detection to breakthrough perioperative imaging technologies that provide real-time guidance for complex cardiac interventions.
Read more

Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions

European Heart Journal
February 16, 2026

Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction

JACC
January 16, 2026

Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis

JACC
November 12, 2025

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings

CJASN
August 25, 2025

Cost-Effectiveness of AI-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the EAGLE Trial

Mayo Clinic Proceedings: Digital Health
October 25, 2024

Artificial Intelligence Evaluation of Electrocardiographic Characteristics and Interval Changes in Transgender Patients on Gender-Affirming Hormone Therapy

European Heart Journal
October 14, 2024

Artificial Intelligence-Enhanced Electrocardiography Identifies Patients With Normal Ejection Fraction at Risk of Worse Outcomes

ScienceDirect
September 27, 2024

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Nature Medicine
September 1, 2024

Predictors of mortality by an artificial intelligence enhanced electrocardiogram model for cardiac amyloidosis

Wiley Online Library
August 30, 2024

Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure

Nature Medicine
January 6, 2024
INTEGRATION

Seamless and Secure

Deploy Anumana directly within your EHR and on your own servers. No PHI data is exported for external processing which simplifies integration and minimizes security and privacy risks.

EHR Integration

EHR Configuration

  • Create ECG-AI orderable and results view for clinical workflow integration.
  • Configure HL7 datapoints for seamless data exchange.
  • Set up CPT III codes for charge capture.

IT Infrastructure

  • Configure virtual machine requirements and network access.
  • Install ECG-AI framework software (including FDA cleared LEF algorithm).

ECG Management System Integration*

Attributes

  • Configure digital waveform files (XML or DICOM) for automated processing.
  • Anumana algorithmic processing occurs entirely within the ECG management system framework.
  • Define customized ECG-AI and ECG outputs as required.

*Available on compatible ECG Management Systems..

WORKFLOW

Uninterrupted, Unchanged

Your institution’s workflow stays exactly the same – no new systems, no disruptions.

  • ECG orders and lab processes remain unchanged.
  • Anumana AI runs securely inside your environment.
  • Instant AI results flow directly into your EHR.
  • Administrators maintain complete data control.

Your ECG, your way – smarter and faster.

LATEST

Anumana in the news

The Real Test for AI Diagnostics Isn’t Performance — It’s Clinical Adoption

Cardiology, in particular, offers a clear view into why so many AI solutions struggle to scale and what differentiates those…
Posted on
March 16, 2026

Anumana Makes Educated Inferences in Heart Failure

Anumana was founded in 2021 as the culmination of a joint effort between the Mayo Clinic and nference, an early…
Posted on
February 1, 2026

Building sEHR-BERT: A Custom Language Model for Structured Electronic Health Records – Adapting transformer architecture for the unique challenges of

Why Standard BERT Isn't Enough for Structured EHRs BERT revolutionized natural language processing, but electronic health records (EHRs) present unique challenges…
Posted on
January 22, 2026

Bridging ECG Signals and EHR data through Contrastive Learning

Introduction: How can we teach an AI to see the connections between a patient’s heart rhythms and their clinical notes?…
Posted on
January 22, 2026

Anumana Appoints Kevin Ballinger and Jean-Luc Butel to Board of Directors

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Anumana Inc., a leader in AI-powered cardiovascular detection algorithms, today announced the appointment of Kevin Ballinger and Jean-Luc Butel…
Posted on
January 12, 2026

Tackling Imbalanced Regression in Clinical AI with KDE-weighted Deep Models

Clinical AI systems often include automated analysis of medical time series, such as electrocardiogram (ECG), to serve as valuable diagnostic…
Posted on
December 6, 2025

Tackling Imbalanced Regression in Clinical AI with KDE-weighted Deep Models

Clinical AI systems often include automated analysis of medical time series, such as electrocardiogram (ECG), to serve as valuable diagnostic…
Posted on
December 5, 2025

Supporting Predictive Analytics to Improve Clinical Decision Making and Patient Outcomes

As we strive to improve clinical decison making and patient outcomes – predictive analytics are a wonderful tool to help us in that goal.…
Posted on
November 26, 2025

Anumana Advances AI-Driven Cardiovascular Science with Late-Breaking Heart Failure Study and Multiple Abstracts at AHA 2025

Simultaneous publication in Journal of the American College of Cardiology demonstrates impact of ECG-AI on heart failure risk assessment. CAMBRIDGE,…
Posted on
November 20, 2025

JACC Featured Science: ECG-AI For HF Risk; ANSWER-HF; NEO-MINDSET; MANIFEST-US

Featured science studies simultaneously published in JACC and presented at AHA 2025 looked at artificial intelligence applied to an electrocardiogram (ECG-AI) for heart failure…
Posted on
November 10, 2025

Let’s talk about how we can work together.

1 Anumana data on file.
2FDA Clearance K250652 for ECG-AI LEF.