ǂa ǂscoping review
Drieda Zace (Author), Federico Semeraro (Author), Sebastian Schnaubelt (Author), Jonathan Montomoli (Author), Giuseppe Ristagno (Author), Nino Fijačko (Author), Lorenzo Gamberini (Author), Elena Giovanna Bignami (Author), Robert Greif (Author), Koenraad G. Monsieurs (Author), Andrea Scapigliati (Author)

Abstract

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.

Keywords

cardiac arrest;resuscitation;artificial intelligence;machine learning;deep learning;large language model;scoping review;

Data

Language: English
Year of publishing:
Typology: 1.02 - Review Article
Organization: UM FZV - Faculty of Health Sciences
Publisher: Elsevier B.V.
UDC: 004.8:616-083.98
COBISS: 234963971 Link will open in a new window
ISSN: 2666-5204
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Downloads: 1
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Other data

Secondary language: Slovenian
Secondary keywords: Umetna inteligenca;
Type (COBISS): Scientific work
Pages: str. 1-11
Issue: ǂVol. ǂ24, [article no.] 100973
Chronology: 2025
DOI: 10.1016/j.resplu.2025.100973
ID: 26861941