Artificial Intelligence in Resuscitation: A Scoping Review

Dmitriy Viderman, Yerkin G. Abdildin, Kamila Batkuldinova, Rafael Badenes, Federico Bilotta

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)

Abstract

Introduction: Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation. Methods: We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits. Results: After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance. Conclusion: AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.

Original languageEnglish
Article number2254
JournalJournal of Clinical Medicine
Volume12
Issue number6
DOIs
Publication statusPublished - Mar 2023

Keywords

  • artificial intelligence
  • cardiac arrest
  • premature mortality
  • resuscitation

ASJC Scopus subject areas

  • General Medicine

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