ReviewInternational journal of critical illness and injury science
Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review.
Review in International journal of critical illness and injury science. 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.
- Assessing acuity in pediatric emergency department triage: performance of a large language model.npj health systems · 2026Article
- Pediatric head injury in the emergency department: balancing radiation risk, missed diagnoses, and decision-rule evolution.International journal of emergency medicine · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) refers to machines capable of imitating human cognition, with abilities to learn, apply logic and reasoning, and adapt to new information. The scope of AI in medicine ranges from prehospital triage to assisting in diagnosis and prognosticating patients. AI has shown incredible potential in pediatric emergency department by focusing on the development of clinical prediction models, triage systems, and diagnostic aids, contributing to higher accuracy and efficiency in patient management, along with hospital management, medical education, and training. Our review article discusses the current applications of AI in pediatric emergency and explores the barriers to AI in health care and ways to circumnavigate them moving forward. We aim to offer an insight into this less-explored world where technology meets the unpredictable and fast-paced environment of pediatric emergency medicine, building a future with a promise of innovation and redefining standards of care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.