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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.02 - Pregledni znanstveni članek
Organizacija: UM FZV - Fakulteta za zdravstvene vede
Založnik: Elsevier B.V.
UDK: 004.8:616-083.98
COBISS: 234963971 Povezava se bo odprla v novem oknu
ISSN: 2666-5204
Št. ogledov: 0
Št. prenosov: 1
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: Umetna inteligenca;
Vrsta dela (COBISS): Znanstveno delo
Strani: str. 1-11
Zvezek: ǂVol. ǂ24, [article no.] 100973
Čas izdaje: 2025
DOI: 10.1016/j.resplu.2025.100973
ID: 26861941