| Title: | Artificial intelligence in resuscitation: a scoping review |
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| Authors: | ID Zace, Drieda (Author)
ID Semeraro, Federico (Author)
ID Schnaubelt, Sebastian (Author)
ID Montomoli, Jonathan (Author)
ID Ristagno, Giuseppe (Author)
ID Fijačko, Nino (Author)
ID Gamberini, Lorenzo (Author)
ID Bignami, Elena G. (Author)
ID Greif, Robert (Author)
ID Monsieurs, Koenraad G. (Author)
ID Scapigliati, Andrea (Author) |
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| Files: |  1-s2.0-S2666520425001109-main.pdf (1,45 MB)
MD5: 929E4B0EBBA420B5C6DC565D4EE1350E
 https://api.elsevier.com/content/article/PII:S2666520425001109?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S2666520425001109?httpAccept=text/plain
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.02 - Review Article |
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| Organization: | FZV - Faculty of Health Sciences
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| 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. |
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| Keywords: | Cardiac arrest, Resuscitation, Artificial intelligence, Machine learning, Deep learning, Large language model, Scoping review |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 27.04.2025 |
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| Article acceptance date: | 29.04.2025 |
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| Publication date: | 03.05.2025 |
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| Publisher: | Elsevier B.V. |
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| Year of publishing: | 2025 |
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| Number of pages: | 11 str. |
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| Numbering: | Letn. 24, št. članka 100973 |
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| PID: | 20.500.12556/DKUM-93795  |
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| ISSN: | 2666-5204 |
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| UDC: | 004.8:616-083.98 |
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| ISSN on article: | 2666-5204 |
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| COBISS.SI-ID: | 234963971 |
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| DOI: | 10.1016/j.resplu.2025.100973 |
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| Publication date in DKUM: | 22.07.2025 |
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| Views: | 235 |
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| Downloads: | 7 |
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| Metadata: |    |
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| Categories: | Misc.
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