Three New Studies Show Viz.ai’s Cardio Suite Speeds Detection of Cardiac Disease and Improves Patient Follow-Up

Key Terms

hypertrophic cardiomyopathy medical

Hypertrophic cardiomyopathy is a genetic heart condition in which the heart muscle becomes abnormally thick, making it harder for the heart to pump and for electrical signals to travel normally; think of a pump whose walls have thickened so it moves less efficiently. Investors care because the condition drives demand for diagnostics, drugs and devices, affects workforce and insurance costs, and can influence clinical trial results, regulatory approvals and liability exposure in healthcare-related companies.

ecg medical

An ECG (electrocardiogram) records the heart’s electrical activity through small sensors on the skin and produces a waveform that shows heart rate, rhythm and signs of stress or damage—think of it as a seismograph for the heart’s electrical signals. Investors care because ECGs are central to diagnosing and monitoring cardiac safety in clinical trials, required for regulatory approval of many drugs and devices, and drive demand for related medical equipment and services.

ai-ecg medical

AI-ECG is the use of artificial intelligence algorithms to analyze electrocardiogram (ECG) signals, spotting patterns or risks in heart activity that a human reader might miss. For investors, it matters because AI-ECG can speed diagnosis, lower costs, and open new markets for software, devices, and data services—like a high-precision spell-checker for heart rhythm that can change how care is delivered, reimbursed, and regulated.

cardio suite medical

A cardio suite is a bundled set of medical devices, software and services designed for diagnosing, monitoring or treating heart conditions, often installed together in a hospital or clinic. For investors, it matters because a suite sells as an integrated product rather than single parts, which can drive larger, recurring sales and service contracts, affect regulatory review and influence how quickly hospitals adopt the technology—like buying a fully furnished kitchen instead of separate appliances.

cardiac mri medical

Cardiac MRI is a noninvasive imaging scan that uses magnetic fields to create detailed pictures of the heart’s structure, blood flow and muscle function. Think of it as a high-resolution camera that lets doctors and researchers see how well the heart is pumping and whether there is scarring or abnormal tissue; those findings can influence treatment decisions, drug trial outcomes and the market value of companies involved in diagnostics, devices or therapies.

echocardiography medical

An echocardiography is a noninvasive test that uses sound waves to create moving images of the heart, showing its size, pumping strength and how the valves work—think of it as an ultrasound camera for the heart. Investors care because these images provide objective measures used to diagnose heart disease, guide treatment decisions, support clinical-trial endpoints and drive demand for medical devices and services, which affects revenue and reimbursement outlooks.

electrocardiogram medical

An electrocardiogram (ECG or EKG) is a simple, noninvasive test that records the heart’s electrical signals through sensors on the skin and displays them as a waveform showing heart rate and rhythm. For investors, ECGs are important because they are a primary tool for diagnosing heart conditions, monitoring safety and effectiveness in clinical trials, and driving demand for medical devices and services—like a dashboard gauge revealing whether a car’s engine is healthy.

phenotype medical

Phenotype is the set of observable traits or measurable characteristics of an organism—such as appearance, behavior, disease signs, or lab measurements—produced by the interaction of its genes and the environment. For investors, phenotype matters because drugs, diagnostics, or agricultural products are judged by the tangible changes they produce; clear, consistent improvements in phenotype (think of a recipe’s final cake rather than the written recipe) help drive regulatory approval, market uptake, and revenue.

See more from StockTitan in Google Search and AI answers. Adds StockTitan as a preferred source · opens Google

Add on Google Not now

Real-world evidence to be presented at the American College of Cardiology Scientific Session 2026 highlights impact of Viz HCM, developed as part of a multi-year agreement with Bristol Myers Squibb, on disease detection and increasing access to life-saving treatments

SAN FRANCISCO--(BUSINESS WIRE)-- Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, today announced that three abstracts featuring Viz HCM, the company’s AI-powered ECG analysis and care coordination solution designed to detect and triage patients with signs of hypertrophic cardiomyopathy (HCM), part of the Viz Cardio Suite, have been accepted for presentation at the American College of Cardiology’s Annual Scientific Session (ACC.26), taking place March 28–30, 2026, in New Orleans.

Together, the studies reinforce the growing evidence that Viz HCM’s AI-ECG technology can help uncover undiagnosed HCM, re-engage patients lost to follow-up, and identify individuals who may benefit from earlier surveillance. With a total of 15 clinical abstracts presented to date, the Viz HCM body of evidence continues to expand across real-world and academic settings. The Viz HCM solution was developed as part of a multi-year agreement between Viz.ai and Bristol Myers Squibb (NYSE: BMY) aimed at transforming how HCM is identified and managed. Viz HCM is the first and only FDA-cleared AI algorithm designed to assist clinicians in detecting signs of HCM from a standard 12-lead ECG.

Nearly 85% of HCM patients are undiagnosed, underdiagnosed, or misdiagnosed, despite major therapeutic advances that have significantly improved patient outcomes.¹ Furthermore, many patients remain undertreated or disconnected from specialty care. The data presented at ACC.26 demonstrates how AI-enhanced ECG analysis may help close these gaps at scale.

“Our real-world study applying Viz.ai’s AI-ECG algorithm for hypertrophic cardiomyopathy suggests that many of these patients could be potentially identified years prior to their initial cardiac MRI in our system,” said Jordan B. Strom, MD, MSc, Medical Director, Echocardiography Laboratory at Beth Israel Deaconess Medical Center and the study’s lead author. “While we need to identify how prospective implementation of this algorithm impacts workflow and practice, AI-ECG presents a promising opportunity for earlier disease detection and appropriate triage.”

The following clinical studies are being presented at ACC.26:

• “Artificial-Intelligence–Enhanced Electrocardiogram Screening to Identify Hypertrophic Cardiomyopathy: Real-World Experience from a Hypertrophic Cardiomyopathy Center Within a Community Health System” evaluated implementation of Viz HCM across The Christ Hospital Health Network, a community health system with an HCM Center of Excellence. The study demonstrated that AI-enabled ECG screening can identify both previously undiagnosed HCM patients and individuals who had fallen out of specialty care. In the real-world deployment, the tool led to 11 new HCM diagnoses. These findings highlight how embedding AI-ECG within a health system can help close diagnosis gaps and strengthen ongoing care coordination.
• “Phenotype Progression for Artificial Intelligence Positive, Phenotype Negative Electrocardiogram Diagnosis in Patients with Hypertrophic Cardiomyopathy” evaluated patients initially designated as AI-positive but phenotype-negative for HCM at Mount Sinai Medical Center in New York City. Among 100 known HCM patients, 13% of individuals initially classified as “false positive” progressed to phenotypic HCM over a mean of 2.74 years. Investigators propose the term “pre-positive” to describe this population and suggest repeat echocardiography within one to three years following an AI-ECG alert. The findings provide early longitudinal insight into how AI-ECG may help identify patients with AI-ECG signals that may precede structural disease detection.
• “Incremental Value of Artificial Intelligence-Enabled Algorithm for Detection of Hypertrophic Cardiomyopathy Compared With a Standard Electrocardiogram” examines the added diagnostic value of AI-ECG compared with standard ECG interpretation. The study highlights how AI-enabled analysis shows improved predictive performance compared with standard ECG measures, reinforcing its potential role as a clinical decision support tool in routine cardiovascular care.

“AI has the potential not only to detect disease earlier, but to also reshape how health systems monitor and follow patients over time,” said Tim Showalter, MD, Chief Medical Officer of Viz.ai. “The research presented at ACC.26 demonstrates how Viz Cardio Suite can drive earlier detection, strengthen follow-up care, and support more proactive disease management.”

To learn more about Viz.ai, visit us at ACC.26 in New Orleans at booth 219.

¹Desai et al., “Real-World Artificial Intelligence–Based Electrocardiographic Analysis to Diagnose Hypertrophic Cardiomyopathy”, JACC: Clinical Electrophysiology (June 2025).

About Viz.ai

Viz.ai is the leader in building and deploying AI-powered Care Pathways and helping doctors do their work. The Viz Platform is deployed in 2,000 hospitals across the United States and trusted by many of the leading life sciences companies. The platform uniquely combines real-time, multimodal clinical data with deep clinician engagement to detect disease earlier, coordinate care teams, and help ensure patients receive the right treatment faster. Viz.ai was the first company awarded CMS reimbursement for AI and is ranked the #1 Healthcare AI Platform by hospitals and health systems in the Black Book Research survey. For more information, visit Viz.ai.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260323235199/en/

Media Contact
Caroline Rueve
viz.ai@solcomms.co

Source: Viz.ai