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

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

Viz.ai Press Release

SAN FRANCISCO -- 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.