ArticleWorld journal of emergency medicine2023
Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence.
Article in World journal of emergency medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unmanned aerial vehicles in prehospital emergency care: current evidence, technological advances, and future perspectives.Frontiers in public health · 2025Pooled it
- Application of artificial intelligence in triage in emergencies and disasters: a systematic review.BMC public health · 2024Pooled it
- Hospital system resilience in the face of armed conflicts; a cross sectional study from two regions in Lebanon.BMC health services research · 2026Observational
- Drone-delivered automated external defibrillators in prehospital emergency care: implementation evidence, bottlenecks, and conceptual integration with artificial intelligence and next-generation communications.Frontiers in public health · 2026Article
- Use of artificial intelligence to support prehospital traumatic injury care: A scoping review.Journal of the American College of Emergency Physicians open · 2024Article
- Glycerophosphoinositol modulates FGA and NOTCH3 in exercise-induced muscle adaptation and colon cancer progression.Frontiers in pharmacology · 2024Article
- Emergency medical services preparedness in mass casualty incidents: A qualitative study.Health science reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid on-site triage is critical after mass-casualty incidents (MCIs) and other mass injury events. Unmanned aerial vehicles (UAVs) have been used in MCIs to search and rescue wounded individuals, but they mainly depend on the UAV operator's experience. We used UAVs and artificial intelligence (AI) to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.
methodsThis was a preliminary experimental study. We developed an intelligent triage system based on two AI algorithms, namely OpenPose and YOLO. Volunteers were recruited to simulate the MCI scene and triage, combined with UAV and Fifth Generation (5G) Mobile Communication Technology real-time transmission technique, to achieve triage in the simulated MCI scene.
resultsSeven postures were designed and recognized to achieve brief but meaningful triage in MCIs. Eight volunteers participated in the MCI simulation scenario. The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.
conclusionThe proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.
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.