ArticleBioData mining2025
Development of an AI-powered AR glasses system for real-time first aid guidance in emergency situations.
Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI-powered augmented reality (AR) systems provide real-time, hands-free guidance, enabling untrained individuals to respond effectively in emergencies. By combining AI decision-making with AR visuals, they enhance awareness, reduce stress, and help bridge the gap before professional help arrives, especially in critical or underserved settings. This paper introduces an AI-powered augmented reality (AIAR) platform designed to assist untrained bystanders in delivering effective emergency response. By combining real-time object detection (YOLOv5), edge computing (Jetson Nano), and multimodal guidance through AR overlays and text-to-speech, the system targets four critical scenarios—bleeding, burns, fainting, and CPR—using specialized detection models built on domain-specific datasets. The decision-making and instructional flows align with medically validated protocols to ensure accuracy and relevance. Testing demonstrated the system’s high usability and responsiveness in delivering hands-free, context-aware guidance, though limitations related to hardware, model performance, and real-world conditions remain. Future developments include more compact AI hardware, wearable AR enhancements, adaptive interaction, clinical trials, and the integration of advanced AI, such as generative models and natural language interaction.
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.