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Review
. 2025 May 3:24:100973.
doi: 10.1016/j.resplu.2025.100973. eCollection 2025 Jul.

Artificial intelligence in resuscitation: a scoping review

Drieda Zace  1 , Federico Semeraro  2 , Sebastian Schnaubelt  3   4   5 , Jonathan Montomoli  6 , Giuseppe Ristagno  7   8 , Nino Fijačko  9 , Lorenzo Gamberini  2 , Elena G Bignami  10 , Robert Greif  11   12 , Koenraad G Monsieurs  13 , Andrea Scapigliati  14
Affiliations
Review

Artificial intelligence in resuscitation: a scoping review

Drieda Zace et al. Resusc Plus. .

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: Artificial intelligence; Cardiac arrest; Deep learning; Large language model; Machine learning; Resuscitation; Scoping review.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FS is the Chair of the European Resuscitation Council, an Emeritus member of the ILCOR BLS Working Group, and a member of the Italian Resuscitation Council Foundation, SS is an ILCOR EIT Task Force member, ERC Advanced Life Support Science and Education Committee member, and Vice Chair of the Austrian Resuscitation Council, JM is a co-founder and shareholder of Callisia srl University Spin-off at Università Politecnica delle Marche developing a smart bracelet collecting patient data intelligently for real-time visualization and data analysis, GR is the Director of Congresses for the European Resuscitation Council, an Emeritus member of the ILCOR BLS Working Group, member of the Italian Resuscitation Council Foundation and Resuscitation Plus Editorial board member. NF Fijačko is a member of the ERC BLS Science and Education Committee, LG is a member of the Scientific Committee of the Italian Resuscitation Council, EGB is the Chair of SIAARTI Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care, RG is ERC Director of Guidelines and ILCOR, ILCOR Task Force chair for Education Implementation and Team and Resuscitation Plus Editorial board member. AS is the President of the Italian Resuscitation Council. KGM, DZ has no conflict of interest.

Figures

Fig. 1
Fig. 1
PRISMA flowchart of the screening and selection process.
Fig. 2
Fig. 2
Distribution map of the country of origin of the studies.
Fig. 3
Fig. 3
The top 10 countries contributing to the research.
Fig. 4
Fig. 4
Distribution of study design categories.
Fig. 5
Fig. 5
Distribution of AI branch categories.
Fig. 6
Fig. 6
Distribution of performance metrics.
See this image and copyright information in PMC

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