• Federico Semeraro, Sebastian Schnaubelt, Malta HansenCarolinaCDepartment of Cardiology Copenhagen University Hospital Herlev and Gentofte, Hellerup, Denmark; Copenhagen Emergency Medical Services, University of Copenhagen, Denmark; Department of Cardiology, Rigshospitalet, University of Copenhag, Elena Giovanna Bignami, Ornella Piazza, and Koenraad G Monsieurs.
• Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy. Electronic address: federicofsemeraro@gmail.com.
• Resuscitation. 2024 Jul 1; 200: 110250110250.

IntroductionCardiac 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.MethodsWe 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.ResultsWe 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.ConclusionIntegrating 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.Copyright © 2024 Elsevier B.V. All rights reserved.

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