Beyond the chain of survival:a scoping review of artifi cial intelligence applications in cardiac arrestOA

BACKGROUND:To provide a comprehensive analysis of the landscape of artifi cial intelligence(AI)applications in cardiac arrest(CA).METHODS:Comprehensive searches were conducted in PubMed,the Cochrane Library,Webof Science,and EMBASE from database inception through 10 June 2025.Studies that applied AI inboth in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA)populations acrossthe following domains were included:prediction of cardiac arrest occurrence,prognostication ofCA outcomes,applications of large language models(LLMs),and evaluation of cardiopulmonaryresuscitation(CPR)and other AI-driven interventions related to CA.RESULTS:The scoping review included 114 studies,encompassing data from 9,574,462patients 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-arrestprognosis 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 identifi cation(n=1).Across allapplication 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 usingextreme gradient boosting(XGBoost)or random forest(RF)models.For CPR-related decisionsupport 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 usingXGBoost,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 andCPR-related support,as well as post-arrest and rehabilitation outcomes.Future research directions includedrug discovery,post-resuscitation management,neurorehabilitation,and clinical trial innovation.Furtherstudies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validatetheir eff ectiveness across diverse patient populations.Overall,AI has signifi cant potential to improve clinicalpractice,and its role in CA application is increasingly important.