What to know:

• Tempus Next Cardiology is an AI-enabled care pathway platform to help clinicians find patients with undiagnosed or undertreated cardiovascular or pulmonary disease, and Tempus Next uses AI to support clinicians in closing care gaps.
• Tempus ECG-AF analyzes recordings of 12-lead electrocardiogram devices to detect signs associated with a patient experiencing AF within the next 12 months, and Tempus ECG-Low EF detects signs associated with having a low left ventricular ejection fraction.
• Tempus Pixel provides advanced viewing and automated reporting of cardiac MR images to help improve efficiency and accuracy, and AI-enabled 4d flow enables reductions in scan time of up to 30%.
• Our initial 2020 study with Geisinger demonstrated that AI can predict mortality directly from ECG data, and a joint study by the Tempus and Geisinger teams demonstrated that AI can predict the risk of new-onset AFib.

Building the ECG of the future with Artificial Intelligence

One way that AI can help is by sifting through and making sense of large amounts of data collected during routine medical care. Along with many clinicians, we at Tempus believe that important insights live within this large amount of ECG data and that those insights can revolutionize the way we use ECGs to help diagnose and treat patients. Our initial 2020 study with Geisinger, published in Nature Medicine, demonstrated that AI can predict mortality directly from ECG data even in the large subset of ECGs interpreted by physicians as normal. This showed us that AI can help physicians uncover patterns in ECGs that the human eye may be missing and that these patterns were linked to important outcomes like mortality. A joint study by the Tempus and Geisinger teams, published last year in Circulation, demonstrated that AI can in fact predict the risk of new-onset AFib. Using 1.6 million ECGs from 430,000 patients collected between 1984 and 2019, we trained a deep neural network to predict which patients were more likely to develop AFib. Importantly, nearly two-thirds of patients without known AFib, who then experienced an AFib-related stroke, were identified as high risk by the model before the stroke occurred. The device automatically analyzes a 12-lead ECG to help physicians identify patients who are at increased risk of developing AFib (and a similar abnormal heart rhythm called atrial flutter) within the next year.
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Cardiology Care Gap Solutions – Tempus 

Tempus Next Cardiology is an AI-enabled care pathway platform to help clinicians find patients with undiagnosed or undertreated cardiovascular or pulmonary disease. Tempus Next ingests multimodal data, runs AI-based algorithms, and surfaces insights for care teams to evaluate and action on. Tempus Next includes screening algorithms for abdominal aortic aneurysms (AAA) and thoracic aortic aneurysms (TAA) using clinician reports from abdominal ultrasounds and CTs, in addition to all other available patient data. Tempus Next Structural Heart capabilities include screening for appropriate care across aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, and pulmonary regurgitation. Within cardiomyopathies, Tempus Next screening protocols address care across non-ischemic dilated cardiomyopathy and cardiac oncologic disease. Tempus Next electrophysiology capabilities include screening for risk of sudden cardiac arrest (SCA), risk of left atrial appendage thrombus, and atrial fibrillation. Tempus Next helps find undiagnosed or undertreated disease and close gaps in care at scale with 350+ EHR connected health systems across 2,000+ hospitals. 100+ hospitals nationwide are currently powered by Tempus Next.
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Cardiology Care Pathway Solutions | AI-Driven Insights | Tempus

Tempus Next uses AI to support clinicians in closing care gaps by identifying patients who have not received treatment consistent with applicable guidelines and delivering customized and accurate notifications directly within an established clinical workflow. AI-enabled solutions surface deep insights from multimodal, longitudinal patient data including medical imaging, EHR data, clinician notes and time series data. Surface precision care pathways at the point of care. In this study, black patients saw a larger decrease in average time from index echo to follow-up (120 to 64 days) compared to white patients (57 to 36 days). This shows the improvement in black patients’ time to follow-up towards the average of all patients (narrowing the gap). Helping clinicians with timely and accurate diagnostics, procedures, and therapies for all patients, regardless of socioeconomic status.
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Cardiology Solutions | AI-Powered ECG & Data Insights | Tempus

Tempus accelerates cardiac care with AI-powered solutions to identify potentially undiagnosed patients. Tempus empowers clinicians to deliver the next step in a patient’s care journey with our AI-enabled care pathway intelligence platform. Pixel for Radiology provides advanced viewing and automated reporting of cardiac MR images help improve efficiency and accuracy in flow visualization and quantification, functional analysis, and tissue characterization. ECG-AI devices integrate with advanced cardiology algorithms to detect signs of disease and notify care teams for patient follow up.
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ECG AI Platform | Predictive Cardiology Insights 

Tempus ECG-AF algorithm is intended for use to analyze recordings of 12-lead electrocardiogram (ECG) devices and detect signs associated with a patient experiencing 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. Tempus ECG-Low EF is software intended to analyze resting, non-ambulatory 12-lead ECG recordings and detect signs associated with having a low left ventricular ejection fraction (LVEF less than or equal to 40%). It is for use on clinical diagnostic ECG recordings collected at a healthcare facility from patients 40 years of age or older at risk of heart failure. This population includes but is not limited to patients with atrial fibrillation, aortic stenosis, cardiomyopathy, myocardial infarction, diabetes, hypertension, mitral regurgitation, and ischemic heart disease.
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Tempus Pixel Cardio | AI-Powered Cardiac Image Analysis

• AI-enabled solution to analyze and quantify cardiac MR images. Tempus Pixel provides advanced viewing and automated reporting of cardiac MR images to help improve efficiency and accuracy in flow visualization and quantification, functional analysis, and tissue characterization.
• AI-enabled 4d flow: Visualize and quantify blood flow precisely anywhere in the heart and its major vessels based on 4D flow datasets, enabling reductions in scan time of up to 30%.
• functional analysis: Automatically quantify cardiac chambers volumetry and ventricular function data from short axis datasets, enabling a reduction in segmentation time of up to 93% per study.
• tissue characterization: Automatically quantify delayed enhancement, perfusion, and parametric mapping images (T1, T2 and T2*), with graphs and 17-segment models for faster and more informed decisions.
• AI-based Myocardial Strain: Calculates strain, strain rate, and myocardial velocity that are reliable, quantitative, visually confirmable, and that separates normal from disease myocardial tissue.

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Tempus Receives U.S. FDA Special 510(k) Clearance for Updated Tempus Pixel Device – 

CHICAGO, September 11, 2025 – Tempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine, today announced it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its updated Tempus Pixel, an AI-powered cardiac imaging platform. This update allows the generation of T1 and T2 inline maps, further enhancing the device’s capabilities for cardiac MR image analysis. Unlike conventional MR images that show only brightness differences, T1 and T2 maps provide precise numerical values to cardiac tissue characteristics, helping clinicians detect conditions such as fibrosis, inflammation, or edema, that may otherwise go undetected.
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The AI Revolution in Electrocardiography: Transforming ECG AI into Predictive Cardiovascular Insights

There’s so much information in the clinical data that we’re not yet fully leveraging… I think of it like an iceberg. 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) known as the MOMENTOUS Study. Machine learning-enabled approaches to drug-induced LQT prediction with current risk scores, including Tisdale and RISQ-PATH, showed high performance. Using 5-fold cross-validation, we trained XGBoost (XGB), ECG-based deep neural network (DNN), and combined models using EHR data and ECG traces to predict QTc ≥500 ms within 1 year. Per the CMS policy to allow payment for certain Software as a Service (SaaS) devices in the Hospital Outpatient setting, CMS has assigned associated procedure codes for assessments with assistive algorithms like Tempus’ ECG-AF (CPT 0764T and CPT 0765T) to APC 5734, which has a Medicare rate of $128.90, effective January 1, 2025.
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Identifying patients with Valvular Heart Disease: A partnership between St.Francis and Tempus

14,960 with echocardiograms in the St. Francis Hospital electronic health records system were reviewed by Tempus Next over 12 months. 388 patients met defined criteria and did not have an existing treatment plan. 100 previously unidentified, untreated patients were referred to the Heart Valve Center for consideration of an intervention by their provider. In a way, [the Tempus Next solution] mines a lot deeper into the data than we would on our own, said Kristin Pasquarello, M.P.A.S., PA-C, Administrative Director of the Heart Valve Center at St. Francis Hospital.
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