Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

Publikation: Bidrag til tidsskriftKommentar/debatForskningfagfællebedømt

  • Federico Semeraro
  • Sebastian Schnaubelt
  • Hansen, Carolina Malta
  • Elena Giovanna Bignami
  • Ornella Piazza
  • Koenraad G. Monsieurs
Introduction
Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.

Methods
We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.

Results
We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.

Conclusion
Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.
OriginalsprogEngelsk
Artikelnummer110250
TidsskriftResuscitation
Vol/bind200
Antal sider8
ISSN0300-9572
DOI
StatusUdgivet - 2024

Bibliografisk note

Funding Information:
No relationship exists between any of the authors and any commercial entity or product mentioned in this manuscript that might represent a conflict of interest. No inducements have been made by any commercial entity to submit the manuscript for publication. All within 3 years of beginning the work submitted. FS is the Chair-Elect of the European Resuscitation Council (ERC), ILCOR BLS Task Force Emeritus member and Italian Resuscitation Council Foundation member. SS is ILCOR Education Implementation and Teams Task Force member, European Resuscitation Council ALS Science Education Committee member, and Vice-Chair of the Austrian Resuscitation Council, CMH is ILCOR BLS Task Force member and received research grants from TrygFonden, Laerdal Foundation, Novo Nordisk Foundation, Helsefonden, Independent Research Fund Denmark, Capital Region of Denmark and Steering Committee Member of the RACE-CARS trial, Duke University. EGB is the Chair-Elect of SIAARTI - Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care, OP is member of the ESAIC Scientific Sub-Committee Ethics.KM is the Chair of the European Resuscitation Council.

Publisher Copyright:
© 2024 Elsevier B.V.

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