AI reads heart rhythms to unmask silent heart disease

NCT ID NCT06511505

NEW Recruiting now Diagnosis Sponsor: Northwestern University Source: ClinicalTrials.gov ↗

First seen Aug 19, 2026 · Last updated Aug 19, 2026

Summary

This trial tests whether an artificial intelligence tool can help doctors detect undiagnosed atrial fibrillation and structural heart disease from routine electrocardiograms (ECGs). About 1,000 healthcare providers are randomly assigned to either receive AI-generated risk alerts for their patients or not, and researchers compare how often new heart conditions are diagnosed. The goal is to see if this AI approach improves detection and guides further testing in everyday clinical practice.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance: AI-enabled ECG screening tool (rECHOmmend and ECG-AF algorithms) that analyzes 12-lead ECG recordings to flag risk of atrial fibrillation and structural heart disease
What this could lead to: If effective, this AI tool could help doctors catch hidden heart conditions earlier, potentially preventing complications like stroke or heart failure.
What could go wrong: The AI may produce false alarms or miss cases, and it's unclear if acting on its alerts will truly improve patient outcomes in real-world settings.

This is an AI summary of the original study and may miss details. Read our disclaimer.

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Conditions

The condition(s) this trial relates to.

Arrhythmias, Cardiac atrial fibrillation cardiac rhythm disease cardiovascular disorder heart valve disorder

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

ARRHYTHMIA ATRIAL FIBRILLATION CARDIOVASCULAR DISEASES VALVULAR DISEASE

Contacts and locations

Locations

Northwestern University
RECRUITING

Chicago, Illinois, 60611, United States

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