AI moves into cardiac arrest care

Cardiac arrest is a critical medical emergency where timely intervention is essential for patient survival and neurological recovery.
The effectiveness of traditional care measures, such as CPR and advanced life support, remains low, especially for out-of-hospital cardiac arrests.
Artificial intelligence (AI) holds great potential for improving various stages of cardiac arrest care but faces challenges in implementation, such as data interpretation and model testing.
Researchers from Sun Yat-sen University reviewed AI applications spanning prediction, resuscitation support, prognosis, language models, emergency call handling, wearable technology, rhythm identification, education, and extracorporeal resuscitation.
The review highlighted that AI is increasingly integrated throughout the cardiac arrest care continuum, moving beyond initial prediction to broader uses.
AI applications in AI include identifying high-risk patients for in-hospital arrests, improving emergency dispatch, providing real-time CPR feedback, and supporting prognosis and rehabilitation planning post-resuscitation.
Challenges persist in areas like data imbalance, algorithmic bias, privacy concerns, and insufficient multicenter clinical testing.
Future research is needed to focus on prospective trials, explainable AI models, and equitable AI deployment in different resource settings.

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