How AI Is changing the world of defibrillators

Defibrillators are used to deliver electrical currents to the heart as treatment for cardiac arrest that can potentially be fatal. AI is making a big impact on how defibrillators can work more efficiently, with machine learning algorithms becoming increasingly accurate with life-saving treatments, according to a recent paper.

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) leverage shock-advice algorithms to distinguish echocardiogram tracings. The data determines whether rhythms are deemed “shockable” or “non-shockable” to decide whether defibrillation is necessary for treatment.

AI can also be used to diagnose the causes of heart attacks, classify heart rhythms without interrupting cardiopulmonary resuscitation (CPR), and predict the success of defibrillation, according to “Role of artificial intelligence in defibrillators: a narrative review,” by researchers from U.K. hospitals and universities.

While the success rate has improved, concerns around cost and high processing power remain a challenge.

How machine learning is implemented in medical applications has evolved over recent years. Currently, supervised machine learning models are still necessary for defibrillator applications. Deep learning replicates the brain’s neural networks with artificial neural networks (ANN), which contain layers of nodes that process input data.

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