ReviewCureus2025
Current and Future Applications of AI in EMS Training: A Scoping Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Toward Child-Centred Artificial Intelligence in Pediatric Emergency Medicine: A Perspective on Clinical Decision Support, Stakeholder Engagement and Education.Pediatric reports · 2026Article
- The Future of Artificial Intelligence in Emergency Medical Services by 2030: An International Consensus Report.Journal of the American College of Emergency Physicians open · 2026Article
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last 15 years, AI has been increasingly utilized in healthcare education. In EMS, AI is being used to train providers for their high-stress, dynamic environments. This scoping review examines current and future applications of AI in EMS training, with a focus on simulation, interventions, personalized learning, and disaster preparedness. A librarian-assisted literature search was conducted across PubMed, Embase, and Web of Science using terms related to EMS, AI, and education. Articles published between January 1, 2010 and March 16, 2025 were screened and selected based on relevance. Studies were reviewed by title, abstract, and full text, followed by data extraction. Currently, AI has been integrated to enhance EMS training through advanced simulation, procedure training, development of personalized learning materials, and disaster response. These improvements are made possible by utilizing different types of AI tools such as machine learning, natural language processing, and large language models. AI tools showed improvements in realism, diagnostic accuracy, feedback delivery, and learner adaptability. It is actively transforming EMS training by strengthening provider readiness, clinical judgment, and educational outcomes. With continued research and development, its integration could further enhance training effectiveness. Ethical and implementation challenges remain, but increased education and awareness can help ensure that AI is used to positively affect patient care.
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.