SynthesisBMC public health2024
Application of artificial intelligence in triage in emergencies and disasters: a systematic review.
Synthesis in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- The Use of Machine Learning in Emergency Care Units: A Systematic Review.Journal of primary care & community healthPooled it
- Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Review
- Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.Current opinion in anaesthesiology · 2026Article
- AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence.Journal of medical Internet research · 2026Article
- Global Emergency Medicine: A Scoping Review of the Literature From 2024.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026Article
- Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study.Frontiers in public health · 2026Article
- Ethical considerations of artificial intelligence in emergency medicine for triage and resource allocation: a scoping review.Clinical and experimental emergency medicine · 2025Article
- Improving Emergency Response: A Comparative Analysis of Traditional vs Artificial Intelligence-assisted Triage Systems in Health Care and Their Impact.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Article
- Review
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Extreme Weather, Vulnerable Populations, and Mental Health: The Timely Role of AI Interventions.International journal of environmental research and public health · 2025Article
- Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.Pharmacy (Basel, Switzerland) · 2025Article
- Timing is survival: modeling how earlier calls improve cardiac arrest outcomes.Frontiers in digital health · 2025Article
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
INTRODUCTION AND
objectiveModern and intelligent triage systems are used today due to the growing trend of disasters and emergencies worldwide and the increase in the number of injured people facing the challenge of using traditional triage methods. The main objective of this study is to investigate the application of artificial intelligence and Technology in the triage of patients injured by disasters and emergencies and the challenges of the implementation of intelligent triage systems.
methodThe present study is a systematic review and follows PRISMA guidelines. The protocol of this study was registered in PROSPERO with the code CRD42023471415. To find relevant studies, the databases PubMed, Scopus and Web of Science (ISI) were searched without a time limit until September 2024. The scientific search engine Google Scholar and the references of the final articles were read manually for the final review.
resultsThe search identified 2,630 articles, narrowing down to 19 high-quality studies on AI in triage, which improved patient care through optimized resource management and real-time data transmission. AI algorithms like OpenPose and YOLO enhanced efficiency in mass casualty incidents, while e-triage systems allowed for continuous vital sign monitoring and faster triaging. AI tools demonstrated high accuracy in diagnosing COVID-19 (94.57%). Implementing intelligent triage systems faced challenges such as trust issues, training needs, equipment shortages, and data privacy concerns.
conclusionDeveloping assessment systems using artificial intelligence enables timely treatment and better resuscitation services for people injured in disasters. For future studies, we recommend designing intelligent triage systems to remove the obstacles in triaging children and disabled people in disasters.
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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.