HeartBeam and Mount Sinai Announce Strategic AI Collaboration to Bring Clinical-Grade Heart Monitoring into the Home

Key Terms

3d ecg medical

A 3D ECG is an advanced way of recording the heart’s electrical activity that creates a three-dimensional map instead of the usual flat tracing, like turning a paper map into a 3D model to see depth and direction. For investors, it signals a diagnostic product or service that can improve detection and localization of heart problems, potentially commanding premium pricing but also facing regulatory approval, clinical validation and adoption hurdles that affect commercial prospects.

12-lead ecg medical

A 12-lead ECG (electrocardiogram) is a common medical test that records the heart’s electrical activity from 12 different viewpoints using sensors on the chest and limbs—like taking a multi-angle snapshot of the heart’s rhythm and signals. It matters to investors because it is a routine tool for detecting heart problems and monitoring safety in clinical trials and product use; abnormal findings can influence regulatory decisions, trial outcomes, or a healthcare company's market value.

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.

deep learning technical

Deep learning is a type of artificial intelligence that uses multiple layers of computer models to recognize patterns and make decisions from large amounts of data, similar to how someone improves at a task by practicing many examples. Investors care because companies that harness deep learning can automate work, improve products, cut costs or create new revenue streams — but adoption also involves investment, data and regulatory risks that can influence profits and valuation.

remote patient monitoring medical

Remote patient monitoring is the ongoing collection and transmission of a person’s health data (such as heart rate, blood sugar, or blood pressure) from home or another non-clinical setting to healthcare providers using connected devices and secure networks. For investors, it matters because it can reduce hospital visits, create steady service and device revenue, and change how care is paid for—think of it as a home security system for health that can lower costs and improve outcomes.

• Accelerates development of personalized cardiac AI on the HeartBeam platform for wellness and clinical applications, including assessing heart attack risk
• Combines Mount Sinai’s world-class AI and clinical expertise with HeartBeam’s groundbreaking 3D ECG signal collection technology
• HeartBeam is the only platform capable of collecting 12-lead ECG data from patients anytime, anywhere, over time – extending access beyond traditional clinical settings
• Positions HeartBeam to expand from symptom-based cardiac rhythm monitoring into AI-enabled disease assessment and management

SANTA CLARA, Calif.--(BUSINESS WIRE)-- HeartBeam, Inc. (NASDAQ: BEAT), a medical technology company focused on transforming cardiac care by providing powerful cardiac insights, today announced a strategic collaboration with the Icahn School of Medicine at Mount Sinai in New York to accelerate the joint development and validation of next-generation AI-ECG algorithms. The collaboration will leverage HeartBeam’s patented ECG platform, which captures the heart’s electrical activity from 3 non-coplanar dimensions. The partnership marks a significant milestone in the Company’s long-term strategy to build an ecosystem around its platform and strengthen its leadership in AI-enabled cardiac monitoring.

A core value driver of the collaboration is HeartBeam’s differentiated ability to generate longitudinal, high-fidelity synthesized 12-lead ECG datasets from patients in the home setting—data that has historically been inaccessible to AI development. This creates a foundation for developing increasingly personalized algorithms earlier in the care journey and enabling 12-lead ECG assessments in real-world settings, supporting both wellness use cases and clinically relevant assessments, such as heart attack risk.

The combination of HeartBeam’s continuously expanding dataset and Mount Sinai’s clinically annotated 12-lead ECG data can accelerate the training and validation of various AI models. Over time, this collaboration will result in a data engine that is expected to support the development of increasingly personalized algorithms—positioning HeartBeam to expand into new clinical indications and reimbursement pathways.

“We believe expanding access to 12-lead ECG data assessment beyond the clinic is one of the biggest opportunities,” said Robert Eno, Chief Executive Officer of HeartBeam. “By pairing our ability to gather high-fidelity real-world ECG data with Mount Sinai’s extensive clinical data resources and AI expertise, we are creating a differentiated cardiac intelligence engine that can scale beyond traditional care settings and broaden the reach of predictive cardiology, ultimately expanding our clinical and commercial opportunity.”

Under the collaboration, HeartBeam’s in-house AI team led by Lance Myers, PhD, a leading authority on AI applications in biosensor technologies, will work closely with Mount Sinai researchers to develop, train and validate a suite of advanced AI-ECG algorithms intended for deployment onto the HeartBeam platform. Joshua Lampert, MD, FACC, a pioneer in AI-ECG and cardiovascular deep learning research and clinician recognized for his excellence in patient care, Vivek Reddy, MD, an internationally renowned electrophysiologist and innovator, and Girish N. Nadkarni, MD, MPH, the Chief AI Officer of the Mount Sinai Health System and Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, will help guide the scientific and clinical development of the partnership.

“While AI-ECG has rapidly progressed as a field over recent years, there is room for improvement in the portability and scalability of such algorithms beyond acquisition devices that require complex multi-electrode systems. Additionally, current approaches struggle to leverage deep learning inference opportunities outside of traditional health care settings, which is where dynamic changes to cardiovascular health first start before patients present for care. The collaboration addresses these vital needs. By combining deep learning tools with the ability to record full 3-dimensional cardiac electrical activity without cables, we can provide clinically meaningful and operationally pragmatic models at scale regardless of environment,” said Dr. Lampert, Cardiac Electrophysiologist, Medical Director of Machine Learning for Mount Sinai Fuster Heart Hospital and Director of Cardiovascular Artificial Intelligence for the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai.

Pairing HeartBeam’s innovative hardware and approach to cardiac waveform engineering with Mount Sinai’s advanced AI and clinical expertise creates a powerful foundation for developing tools that dynamically meet modern patient and clinician needs wherever they are.

“Heart disease doesn’t only show up during a brief visit to the clinic. This collaboration gives us an opportunity to bring powerful clinical-grade heart monitoring into patients’ daily lives,” said Dr. Nadkarni. “By combining advanced AI with HeartBeam’s ability to capture full 12-lead ECG signals from home over time, we can study the heart in ways that simply haven’t been possible before—helping clinicians detect risk earlier and guide care more precisely.”

“This collaboration addresses an important need by leveraging deep learning and 3-dimensional waveform data for scalable diagnostic and predictive purposes, allowing insights beyond even expert human ability,” added Dr. Reddy, who serves as the Director of Cardiac Arrhythmia Services for Mount Sinai Health System.

Together, the two organizations aim to accelerate the development of high-value algorithms that can be deployed broadly across HeartBeam’s platform. These AI models may include patient-relevant wellness insights, condition-focused assessments, and applications for chronic condition management. By enabling AI models to operate on longitudinal, real-world synthesized 12-lead ECG data rather than isolated clinical snapshots, the collaboration has the potential to significantly expand the addressable market for AI-driven cardiac monitoring. The collaboration could unlock new opportunities in preventive cardiology, chronic disease management, and remote patient monitoring—further reinforcing HeartBeam’s position as a leader in cardiac intelligence platforms.

About the Mount Sinai Health System

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with 48,000 employees working across seven hospitals, more than 400 outpatient practices, more than 600 research and clinical labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time—discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it.

Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients’ medical and emotional needs at the center of all treatment. The Health System includes approximately 9,000 primary and specialty care physicians and 10 free-standing joint-venture centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida. Hospitals within the System are consistently ranked by Newsweek’s^® “The World’s Best Smart Hospitals, Best in State Hospitals, World Best Hospitals and Best Specialty Hospitals” and by U.S. News & World Report's^® “Best Hospitals” and “Best Children’s Hospitals.” The Mount Sinai Hospital is on the U.S. News & World Report^® “Best Hospitals” Honor Roll for 2025-2026.

For more information, visit https://www.mountsinai.org/ or find Mount Sinai on Facebook, Instagram, LinkedIn, X, and YouTube.

About HeartBeam, Inc.

HeartBeam, Inc. (NASDAQ: BEAT) is a medical technology company dedicated to transforming the detection and monitoring of critical cardiac conditions. The Company is creating the first-ever cable-free device capable of collecting ECG signals in 3D, from three non-coplanar directions, and synthesizing the signals into a 12-lead ECG. This platform technology is designed for portable devices that can be used wherever the patient is to deliver actionable heart intelligence. Physicians will be able to identify cardiac health trends and acute conditions and direct patients to the appropriate care – all outside of a medical facility, thus redefining the future of cardiac health management. HeartBeam’s 3D ECG technology received FDA clearance for arrhythmia assessment in December 2024 and the 12-Lead ECG synthesis software in December 2025¹. The Company holds over 20 issued patents related to technology enablement. For additional information, visit HeartBeam.com.

Forward-Looking Statements

All statements in this release that are not based on historical fact are "forward-looking statements." While management has based any forward-looking statements included in this release on its current expectations, the information on which such expectations were based may change. Forward-looking statements involve inherent risks and uncertainties which could cause actual results to differ materially from those in the forward-looking statements, as a result of various factors including those risks and uncertainties described in the Risk Factors and in Management’s Discussion and Analysis of Financial Condition and Results of Operations sections of our Forms 10-K, 10-Q and other reports filed with the SEC and available at www.sec.gov. We urge you to consider those risks and uncertainties in evaluating our forward-looking statements. We caution readers not to place undue reliance upon any such forward-looking statements, which speak only as of the date made. Except as otherwise required by the federal securities laws, we disclaim any obligation or undertaking to publicly release any updates or revisions to any forward-looking statement contained herein (or elsewhere) to reflect any change in our expectations with regard thereto or any change in events, conditions or circumstances on which any such statement is based.

¹Cleared Indications for Use

The HeartBeam System with 12-Lead ECG synthesis software for arrhythmia assessment received FDA clearance in December 2025. Refer to the Company’s Cleared Indications for Use at https://www.heartbeam.com/indications for details on the intended use of its technology.

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Source: HeartBeam, Inc.