20.500.12556/DKUM-93795 Artificial intelligence in resuscitation: a scoping review Background Artificial intelligence (AI) is increasingly applied in medicine, with growing interest in its potential to improve outcomes in cardiac arrest (CA). However, the scope and characteristics of current AI applications in resuscitation remain unclear. Methods This scoping review aims to map the existing literature on AI applications in CA and resuscitation and identify research gaps for further investigation. PRISMA-ScR framework and ILCOR guidelines were followed. A systematic literature search across PubMed, EMBASE, and Cochrane identified AI applications in resuscitation. Articles were screened and classified by AI methodology, study design, outcomes, and implementation settings. AI-assisted data extraction was manually validated for accuracy. Results Out of 4046 records, 197 studies met inclusion criteria. Most were retrospective (90%), with only 16 prospective studies and 2 randomised controlled trials. AI was predominantly applied in prediction of CA, rhythm classification, and post-resuscitation outcome prognostication. Machine learning was the most commonly used method (50% of studies), followed by deep learning and, less frequently, natural language processing. Reported performance was generally high, with AUROC values often exceeding 0.85; however, external validation was rare and real-world implementation limited. Conclusions While AI applications in resuscitation demonstrate encouraging performance in prediction and decision support tasks, clear evidence of improved patient outcomes or routine clinical use remains limited. Future research should focus on prospective validation, equity in data sources, explainability, and seamless integration of AI tools into clinical workflows. Cardiac arrest Resuscitation Artificial intelligence Machine learning Deep learning Large language model Scoping review srčni zastoj oživljanje umetna inteligenca strojno učenje globoko učenje velik jezikovni model pregled obsega true true true Elsevier B.V. Angleški jezik Slovenski jezik Znanstveno delo 2025-07-21 18:35:31 2025-07-22 07:31:35 2025-07-23 03:11:06 0000-00-00 00:00:00 2025 0 0 11 str. Letn. 24, št. članka 100973 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-04-27 2025-04-29 2025-05-03 2666-5204 004.8:616-083.98 234963971 10.1016/j.resplu.2025.100973 2666-5204 1-s2.0-S2666520425001109-main.pdf 1-s2.0-S2666520425001109-main.pdf 1 929E4B0EBBA420B5C6DC565D4EE1350E bf331974fb81fc28dcc02b324d7806f0094156cc964f7c0ca023ec808f451cad d34fa7d6-6650-11f0-bcda-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=193881 https://api.elsevier.com/content/article/PII:S2666520425001109?httpAccept=text/xml 1 b79aaa15-6650-11f0-bcda-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=193879 https://api.elsevier.com/content/article/PII:S2666520425001109?httpAccept=text/plain 1 b79ca03d-6650-11f0-bcda-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=193880 Fakulteta za zdravstvene vede 0 0 0