摘要BACKGROUND:To provide a comprehensive analysis of the landscape of artificial intelligence(AI)applications in cardiac arrest(CA).METHODS:Comprehensive searches were conducted in PubMed,the Cochrane Library,Web of Science,and EMBASE from database inception through 10 June 2025.Studies that applied AI in both in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA)populations across the following domains were included:prediction of cardiac arrest occurrence,prognostication of CA outcomes,applications of large language models(LLMs),and evaluation of cardiopulmonary resuscitation(CPR)and other AI-driven interventions related to CA.RESULTS:The scoping review included 114 studies,encompassing data from 9,574,462 patients in total.AI was most commonly applied to the prediction of CA(overall,n=40;IHCA,n=30;OHCA,n=4;and both,n=6),CPR-related decision support during CA(n=16),and post-arrest prognosis and rehabilitation outcomes(overall,n=38;OHCA,n=21;IHCA,n=3;and both,n=14).Additional application areas included LLM-based applications(n=8),emergency call handling(n=4),wearable device-based detection(n=3),heart rhythm identification(n=2),education(n=2),and extracorporeal cardiopulmonary resuscitation(ECPR)candidate identification(n=1).Across all application scenarios,the highest area under the receiver operating characteristic curve(AUROC)value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron(MLP)model,whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 using extreme gradient boosting(XGBoost)or random forest(RF)models.For CPR-related decision support during CA,the highest AUROC achieved was 0.990 with a convolutional neural network(CNN)model.In prognostic prediction,the optimal AUROC for IHCA patients was 0.960 using XGBoost,while for OHCA patients it reached 0.976 using an MLP model.CONCLUSION:This review shows that AI is most commonly used for the prediction of CA and CPR-related support,as well as post-arrest and rehabilitation outcomes.Future research directions include drug discovery,post-resuscitation management,neurorehabilitation,and clinical trial innovation.Further studies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validate their effectiveness across diverse patient populations.Overall,AI has significant potential to improve clinical practice,and its role in CA application is increasingly important.