
AI Identifies Hidden Signs of Sudden Cardiac Death
A new wave of AI models is finding invisible patterns in routine heart tests to predict sudden cardiac arrest before it strikes.
Discover the newest research about AI innovations in 🫀 Cardiology.

A new wave of AI models is finding invisible patterns in routine heart tests to predict sudden cardiac arrest before it strikes.

A new machine learning model proves that tracking silent organ damage is far more predictive of survival than standard blood pressure readings.

A routine test that costs pennies can now spot hidden heart failures before symptoms even appear.

An overlooked hormonal condition drives millions of hypertension cases, but a new algorithmic approach could finally bring it into the light.

A massive new study shows AI can spot cardiovascular danger on routine mammograms, but the technology faces a major reality check when compared to standard clinical risk calculators.

Smartwatches and rings are marketed as lifesaving heart monitors, but new data shows their single-lead AI systems fail the very patients who need them most.

A new deep learning model proves that artificial intelligence does not just mimic old diagnostic rules to spot deadly heart valve disease—it finds hidden signals doctors have been missing for decades.

A new machine learning model stops doctors from misdiagnosing cardiac amyloidosis as more common heart conditions.

A new machine learning model uses dual ultrasound measurements to identify severe liver damage, offering a way to bypass painful and risky biopsies.

A new AI model uses standard heart scans to calculate your biological age, spotting hidden heart risks even when using cheap wearable devices.