Pathway Labs' EchoNext Receives World's First FDA Clearance for AI Detection of Hidden Structural Heart Disease from Routine ECGs
核心洞察
EchoNext (搜索), developed at NewYork-Presbyterian and Columbia University, is the first FDA-cleared AI tool that reads standard 12-lead ECGs to flag six types of structural heart disease (搜索).
In a head-to-head study, EchoNext (搜索) identified 77% of structural heart problems versus 64% accuracy by cardiologists, and was trained on over 700,000 ECG-echocardiogram pairs.
Nature Medicine published the first peer-reviewed case where AI detection of undiagnosed heart failure (搜索) by EchoNext (搜索) led to a heart transplant.
An artificial intelligence tool that can detect hidden structural heart disease (搜索) from routine electrocardiograms has received the world's first FDA clearance for multicondition AI in cardiology, marking a significant advance in screening for the leading cause of death worldwide.
Pathway Labs (搜索) today announced the public launch of EchoNext (搜索), an AI-powered screening tool developed by researchers at NewYork-Presbyterian and Columbia University Irving Medical Center. The technology reads standard 12-lead ECGs to flag patients at high risk for six types of structural heart disease (搜索), including right and left-sided heart failure (搜索), valve disease (搜索), severe hypertrophy compatible with infiltrative cardiomyopathy (搜索), and pulmonary hypertension (搜索).
"While we have mammograms and colonoscopies for cancer, we have never had an equivalent form of early detection for the most common cause of death in the world — heart disease," said Dr. Pierre Elias, founder and CEO of Pathway Labs (搜索), medical director for artificial intelligence at NewYork-Presbyterian, and assistant professor of medicine and biomedical informatics at Columbia University Vagelos College of Physicians and Surgeons. "Through EchoNext (搜索), we are able to detect high-risk conditions that the human eye can't and may otherwise be missed."
Bridging a Critical Diagnostic Gap
The ECG is the most widely used cardiac test in healthcare, measuring electrical activity in the heart to detect abnormal rhythms, blocked coronary arteries, and prior heart attacks. However, ECGs have traditionally been unable to detect structural heart disease (搜索) — conditions such as valve disease (搜索), cardiomyopathy (搜索), and pulmonary hypertension (搜索) that require echocardiography for definitive diagnosis.
EchoNext (搜索) was designed to bridge this gap by analyzing ECG data to determine when follow-up with cardiac ultrasound is warranted. The deep learning model was trained on more than 700,000 ECG-echocardiogram pairs across NewYork-Presbyterian's healthcare system and has demonstrated robust performance in studies representing over 20 hospitals and 500,000 patients in the United States and Canada.
Clinical Validation and Performance
In a 2025 validation study published in Nature, EchoNext (搜索) accurately identified structural heart disease (搜索) from ECG readings more often than cardiologists, including those who used AI to help interpret the data. In a head-to-head comparison with 13 cardiologists evaluating 3,200 ECGs, EchoNext correctly identified 77% of structural heart problems, while cardiologists making diagnoses with the same ECG data achieved an accuracy of 64%.
The tool demonstrated high accuracy across four hospital systems, including several NewYork-Presbyterian campuses, in identifying a range of structural heart problems, including heart failure (搜索) due to cardiomyopathy (搜索), valve disease (搜索), pulmonary hypertension (搜索), and severe thickening of the heart.
Landmark Clinical Case
On June 22, 2026, Nature Medicine published a world-first peer-reviewed case in which EchoNext (搜索) detected undiagnosed heart failure (搜索) that had been otherwise missed, ultimately leading to the world's first heart transplant due to AI detection of disease. The case represents the first peer-reviewed account of its kind.
"We sought to develop, validate, and deploy AI technologies that would meaningfully change the way we take care of patients," said Dr. Elias. "We're now at a point where we can see that impact and meet patients who are benefiting from these technologies every day."
Partnership with OpenEvidence (搜索) for Broad Deployment
Pathway Labs (搜索) also announced a partnership with OpenEvidence (搜索), a clinical decision platform that serves over 500,000 physicians nationwide. The collaboration places EchoNext (搜索)'s screening output in front of physicians using tools they already access at the point of care.
"FDA-approved AI shouldn't sit siloed in the ivory tower while patients wait years for it to reach them. Putting EchoNext (搜索) on OpenEvidence (搜索) means a breakthrough in heart disease detection is available everywhere care happens, from major hospitals to community practices," said Travis Zack, Chief Medical Officer of OpenEvidence.
Financing to Scale Implementation
Alongside the launch and FDA clearance, Pathway Labs (搜索) announced an $8.5 million seed round led by AlleyCorp (搜索) and Breyer Capital (搜索). The funding will support expansion across health systems, growth of clinical and commercial teams, and ongoing research and development. NewYork-Presbyterian also provided funding as part of this round.
"One of the most compelling opportunities in medical AI is uncovering clinically meaningful signals from data we already collect. Pathway Labs (搜索) is redefining what can be discerned from one of the most widely ordered tests in medicine, the ECG, to surface structural heart disease (搜索) that is otherwise imperceptible to the human eye," said Dr. Morgan Cheatham, Partner and Head of Healthcare and Life Sciences at Breyer Capital (搜索).
Dr. Alexi Nazem, MD, General Partner at AlleyCorp (搜索), added: "Pathway Labs (搜索) is going to save and improve so many lives by diagnosing heart disease sooner and enabling more effective therapeutic intervention. This remarkable technology is pioneering a new type of medicine where AI enables unprecedented capabilities, detecting latent signals in standard diagnostic tests."
Rachel Katz, co-founder and Chief Operating Officer of Pathway Labs (搜索), emphasized the implementation focus: "As a second-time founder, I've seen how often great clinical innovation stalls at implementation. Our job with this capital is to get this into real workflows, across real health systems, at national scale."
These efforts are part of a broader clinical research program, including large-scale trials across emergency departments, aimed at understanding how AI-enabled screening can be integrated into routine care and improve patient outcomes.