Artificial intelligence in cardiopulmonary resuscitation: a systematic review
Abstract
Cardiac arrest remains a major cause of mortality and neurological disability, and its management depends on rapid recognition, effective resuscitation, and accurate post-arrest prognostication. Artificial intelligence (AI) has been increasingly investigated as a tool to support these stages of care. This narrative review summarizes recent evidence on AI applications in cardiac arrest, including prediction, recognition, intra-arrest support, prognostication, and cardiovascular risk stratification. Searches were conducted in PubMed/MEDLINE, Google Scholar, EMBASE, Scopus, IEEE Xplore, and ScienceDirect for studies published from January 2000 to June 2025. Studies addressing AI, machine learning, or deep learning in cardiac arrest-related contexts were descriptively synthesized according to clinical application, AI methodology, and care setting. Overall, 59 studies were included. Six application categories were identified: early prediction of cardiac arrest or clinical deterioration (n = 16), cardiac arrest recognition and emergency response activation (n = 3), intra-arrest CPR and defibrillation support (n = 6), post-cardiac arrest prognostication (n = 20), risk stratification for sudden cardiac death and malignant arrhythmias (n = 11), and related cardiovascular or perioperative prognostic models (n = 3). Deep learning and neural-network-based approaches were the most frequent methodologies (n = 35), followed by traditional machine learning (n = 18) and natural language…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 35 participants or records.
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