ORDER A TEST Log In Contact Us

Tempus ECG-AI Solutions

Tempus accelerates cardiac care with AI-powered solutions to identify potentially undiagnosed patients.

image description image description

ECG-AI devices integrate with advanced cardiology algorithms to detect signs of disease and notify care teams for patient follow up

  • Step 1: Patient Filtering
    Step 1: Patient Filtering

    Tempus employs EHR integrations and patient clinical data to filter patients that may be eligible for ECG-AI device use based on IFU criteria.

  • Step 2: Patient Diagnosis
    Step 2: Patient Diagnosis

    Tempus is pursuing development of ECG-AI based cardiology algorithms that can analyze physiological inputs using machine learning models, and detect the likelihood of a cardiovascular disease.

  • Step 3: Patient Follow Up
    Step 3: Patient Follow Up

    Tempus uses AI to identify and contextualize patients in their journey, surface precision pathways at the point of care and track patients for further referral or diagnostic follow-up.

Tempus ECG-AF

Tempus’ FDA Cleared ECG-AI Device

The Tempus ECG-AF algorithm analyzes recordings of 12-lead electrocardiogram devices and detect signs associated with a patient experiencing atrial fibrillation or flutter within the next 12 months.1

Read more
  • Algorithm Design

    The Tempus ECG-AF model was trained on data from >1,500,000 ECGs and >450,000 patients.2

  • Results

    For patients receiving a positive result from the test, AF would be observed in approximately 1-in-5 patients within the next 12 months.3

  • Integration

    ECG-AF works with existing ECG platforms and EHRs to enable advanced AI deployment  and insights within clinical workflows.

The Tempus ECG-AF model was trained on data from >1,500,000 ECGs and >450,000 patients.2

For patients receiving a positive result from the test, AF would be observed in approximately 1-in-5 patients within the next 12 months.3

ECG-AF works with existing ECG platforms and EHRs to enable advanced AI deployment  and insights within clinical workflows.

Reimbursement Available for ECG-AF

  • Effective January 1, 2025, Medicare has indicated it will reimburse hospital outpatient facilities for assistive algorithmic electrocardiogram risk-based assessments, such as Tempus ECG-AF, for cardiac dysfunction.

    Learn more
    Read press release
image description

Predictive ECG Algorithms for Undiagnosed Disease

Tempus’ portfolio aims to detect underdiagnosis in multiple cardiology disease areas.

image description image description

Pipeline current as of 2/2025

OUR RESEarch

Advancing cardiac care through algorithm development, validation, and scaled clinical adoption

ALERT Study: Addressing Undertreatment and Health Equity in Aortic Stenosis Using an Integrated EHR Platform (ALERT) Study

ACTIVE STUDY

This multi-center, prospective, cluster-randomized controlled trial will evaluate automated notifications as an intervention to support identification and evaluation of patients possibly indicated for a transcatheter or surgical procedure to treat aortic stenosis or mitral regurgitation.

Learn more

MOMENTOUS Study: Assessing the Clinical Impact of an ECG Algorithm to Increase the Diagnosis of Pulmonary Hypertension

ACTIVE STUDY

Tempus is sponsoring a multi-site study of an investigational AI algorithm that analyzes the results of a 12-lead electrocardiogram (ECG) to find patients at increased risk of having undetected pulmonary hypertension (PH). Clinicians will evaluate the AI’s ability to detect patients at risk of undiagnosed PH, and will track clinical outcomes of patients who are identified for further evaluation.

Learn more

NOTABLE Study

ACTIVE STUDY

The NOTABLE (NOrthwestern Tempus AI-enBLed Electrocardiography) study will examine rates of new disease diagnosis, therapeutic interventions, and cardiovascular outcomes in patients managed by clinicians at Northwestern Medicine who use ECG predictive models compared to patients managed by clinicians at Northwestern Medicine who do not use the ECG predictive models.

Learn more

ECG AID Study

CLOSED FOR RECRUITING

This study, which recently completed enrollment, will investigate the prospective performance of our algorithms for atrial fibrillation and structural heart disease.

Learn more

Research as a Service

Automated methods to find patients indicated for trials.

Publications and conference abstract support.

Learn more about joining a study or partnering with Tempus on research.

Learn more
image description

Featured News and Content

view all news
  • UPCOMING WEBINAR:

    AI algorithms in cardiology: Navigating the path from research to practice

    Speakers David Ouyang, MD from Cedars Sinai and Brandon Fornwalt, MD PhD and John Pfeifer, MD, MPH from Tempus discussed the importance of ECG AI algorithms, the journey through algorithm development and validation and the implications for cardiology clinical practice.

    Watch replay
  • UPCOMING WEBINAR:

    Tempus Receives U.S. FDA 510(k) Clearance for Tempus ECG-AF, an AI-based Algorithm that Identifies Patients at Increased Risk of AFib

    Tempus AI, Inc. (NASDAQ: TEM), a leader in artificial intelligence and precision medicine, today announced it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its Tempus ECG-AF device that uses AI to help identify patients who may be at increased risk of atrial fibrillation/flutter (AF).

    Link
  • UPCOMING WEBINAR:

    New CMS Decision Provides Medicare Coverage for the Clinical Use of the Tempus ECG-AF Device

    Tempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine and patient care, today announced the impact of a new decision by the Centers for Medicare and Medicaid Services (CMS) that will allow reimbursement for assessments of cardiac dysfunction using the Tempus ECG-AF algorithm.

    Link
  • UPCOMING WEBINAR:

    Tempus and Northwestern Medicine Announce Collaboration to Bring Practical Application of AI to Healthcare

    Tempus, a leader in artificial intelligence and precision medicine, and Northwestern Medicine, Chicago’s premier integrated academic health system, today announced a collaboration that aims to explore the application of artificial intelligence in clinical care and research.

    Link
  • UPCOMING WEBINAR:

    Tempus Announces Collaboration with United Therapeutics to Study Use of AI to Detect Patients at Risk for Pulmonary Hypertension

    Tempus AI, Inc. (NASDAQ: TEM), a leader in artificial intelligence and precision medicine, today announced a new collaboration with United Therapeutics (UT), a leading biotechnology company focused on providing a brighter future for patients through the development of novel pharmaceuticals and technologies.

    Link

Our Science

view all
  • UPCOMING WEBINAR:

    ECG-based Machine Learning Model Identifies Patients at High Risk for Incident Pulmonary Hypertension

    05/22/2024

    Read more
  • UPCOMING WEBINAR:

    Prospective evidence generation via ECG-AID Study

    Read more
  • UPCOMING WEBINAR:

    rECHOmmend: An ECG-based machine learning approach for identifying patients at increased risk of undiagnosed structural heart disease detectable by Echocardiography

    05/09/2022

    Read more
  • UPCOMING WEBINAR:

    ML-based cluster analysis of patients with significant TR reveals distinct population with different phenotypes and clinical outcomes, American Society of Echocardiography

    6/20/2021

    Read more
  • UPCOMING WEBINAR:

    Deep neural networks can predict new-onset Atrial Fibrillation from the 12-lead ECG and help identify those at risk of Atrial Fibrillation-related stroke

    02/16/2021

    Read more
  • UPCOMING WEBINAR:

    Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

    06/26/2020

    Read more
  1. INDICATIONS FOR USE: Tempus ECG-AF is intended for use to analyze recordings of 12-lead ECG devices and detect signs associated with a patient experiencing atrial fibrillation and/or atrial flutter (collectively referred to as AF) within the next 12 months. It is for use on resting 12-lead ECG recordings collected at a healthcare facility from patients: 65 years of age or older, without pre-existing or concurrent documentation of atrial fibrillation and/or atrial flutter, who do not have a pacemaker or implantable cardioverter defibrillator, and who did not have cardiac surgery within the preceding 30 days. Performance of repeated testing of the same patient over time has not been evaluated and results SHOULD NOT be used for patient monitoring. Tempus ECG-AF only analyzes ECG data. Results should be interpreted in conjunction with other diagnostic information, including the patient’s original ECG recordings and other tests, as well as the patient’s symptoms and clinical history. Tempus ECG-AF is not for use in patients with a history of AF, unless the AF occurred after a cardiac surgery procedure and resolved within 30 days of the procedure. It is not for use to assess risk of occurrence of AF related to cardiac surgery. Results do not describe a person’s overall risk of experiencing AF or serve as the sole basis for diagnosis of AF, and should not be used as the basis for treatment of AF. Results are not intended to rule-out AF or the need for follow-up. Link to IFU
  2.  Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., Nemani, A., Carbonati, T., Johnson, K. W., Young, K., Good, C. W., Pfeifer, J. M., Patel, A. A., Delisle, B. P., Alsaid, A., … Fornwalt, B. K. (2020). Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature Medicine, 26(6), 886–891. https://doi.org/10.1038/s41591-020-0870-z
  3. Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., Rocha, D. B., Stoudt, N. J., Schneider, G., Johnson, K. W., Zimmerman, N., Leader, J. B., Kirchner, H. L., Griessenauer, C. J., Hafez, A., … Haggerty, C. M. (2021). Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke. Circulation, 143(13), 1287–1298. https://doi.org/10.1161/CIRCULATIONAHA.120.047829

Partnering with Tempus is investing in the future

We believe that AI-enabled solutions can help shorten the diagnostic odyssey and improve the lives and outcomes of patients with cardiovascular disease.