ReviewVascular health and risk management2025
Artificial Intelligence in Cardiopulmonary Resuscitation: Revolutionizing Resuscitation Through Precision and Prediction - A Narrative Review.
Review in Vascular health and risk management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiopulmonary resuscitation (CPR) remains a fundamental intervention in the management of cardiac arrest, with timely initiation and optimal performance serving as critical determinants of survival and neurological recovery. In recent years, the application of artificial intelligence (AI) has emerged as a promising approach to enhance the effectiveness of CPR by delivering real-time feedback, supporting clinical decision-making, and enabling individualized resuscitation strategies. This narrative review summarizes the current applications of AI in CPR, including rhythm interpretation, quality monitoring, and post-resuscitation care. In addition, it discusses major challenges related to data privacy, system interoperability, algorithmic fairness, and ethical governance. Future directions emphasize the importance of interdisciplinary collaboration, rigorous model validation, and structured training for clinical users. Although additional validation is required, the integration of AI into resuscitation practices holds significant promise for enhancing patient outcomes and potentially reducing mortality associated with sudden cardiac arrest.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.