June 25, 2026 · 9 min read

AI vs Cardiologist: Who Catches Arrhythmias First?

Every six months a study lands with the same headline: AI beats cardiologists at reading ECGs. The studies are real. The headline is not the whole story. Here is the honest version — what AI catches faster, where it fails badly, and why the doctor-in-the-loop model is not going away.

What AI is genuinely better at

Deep-learning ECG models trained on millions of traces consistently match or outperform cardiologists on three specific tasks: detecting atrial fibrillation from a single lead, flagging left ventricular dysfunction from an apparently normal ECG, and estimating biological heart age. The reason is simple — a model can sweep every beat in a 14-day trace in seconds. A cardiologist cannot.

Where AI quietly fails

• Artefact. A loose electrode looks like ventricular tachycardia to a naive model. Patients get terrified by false alarms.
• Out-of-distribution rhythms. Rare arrhythmias underrepresented in training data are missed silently.
• Context. The same ECG means different things in a 24-year-old athlete and a 68-year-old post-MI patient. AI doesn't know.
• Clinical decision. Detecting an arrhythmia is not the same as deciding what to do about it.

The doctor-in-the-loop model

The honest answer to who catches arrhythmias first is: AI does, on most things, most of the time. The honest answer to who you should trust with your heart is: AI triaging into a cardiologist's review queue. That is the model regulators (CDSCO, FDA, MHRA) are increasingly insisting on, and it is the model Zayra is built around.

How Zayra implements it

Continuous ECG from the Axiom patch is screened in real time by Alyna AI. Flagged events are sent to a cardiologist before any alert reaches the patient — so users receive medically reviewed signals, not raw model output. The platform is in clinical evaluation at PGIMER Chandigarh.