ReviewResuscitation plus2026
Artificial Intelligence in cardiopulmonary resuscitation training - A scoping review.
Review in Resuscitation plus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- A Real-Time Digital Twin Synchronization Framework for Multi-Sensor Cardiopulmonary Resuscitation Measurement.Sensors (Basel, Switzerland) · 2026Article
- Establishing AI-driven evaluation standards and human-in-the-loop guidance for digital first aid education: a perspective.Frontiers in public health · 2026Article
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
Objectives: This scoping review aimed to identify Artificial Intelligence methods used in cardiopulmonary resuscitation (CPR) training. Methods: Members of the writing group 'Education for Resuscitation' of the European Resuscitation Council 2025 guidelines used the PICOST format for this scoping review, which included only published randomized and non-randomized studies. Medline, Embase, Cochrane, Education Resources Information Center, Web of Science, and PubMed were searched from inception to July 2025. Title and abstract screening, full-text review, and data extraction were performed by two researchers in pairs. PRISMA reporting standards were followed. The review was registered at PROSPERO. Because the evidence was insufficient for a systematic review, we changed our initial plan and performed a scoping review. Results: The search identified 6977 citations. After removing 2521 duplicates, reviewing titles and abstracts yielded 43 articles for full-text review. Of these, 15 studies were included in the final analysis. Our findings reveal that Artificial Intelligence is being explored across key areas of CPR training, including its accuracy in detecting CPR quality parameters, providing real-time feedback, creating personalized training experiences, detecting and analyzing dialog segments during and after simulation, generating medical teaching illustrations, its capacity for interactive simulations, and answering laypersons' medical questions. Conclusion: Artificial Intelligence shows potential for transforming CPR training via enhancing real-time feedback, enabling personalized learning, improving dialog analysis, facilitating content creation, and serving as an information source. The current evidence is dominated by proof-of-concept studies. Future research needs to establish the efficacy of Artificial Intelligence-supported CPR training compared to traditional methods.
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.