Novel Technologies to Assist Emergency Medical Care

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Emergency Medicine".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 257

Special Issue Editors


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Guest Editor
Department of Emergency Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
Interests: pediatric emergency medicine; geriatric emergency medicine; resuscitation; hyperbaric medicine; toxicology; big data in healthcare; metaverse in medicine

E-Mail Website
Guest Editor
Department of Emergency Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
Interests: critical care; airway; resuscitation; emergency medical service; artificial intelligence

Special Issue Information

Dear Colleagues,

Emergency medical care is a rapidly evolving field where technological advancements play a crucial role in improving patient outcomes, reducing response times, and enhancing diagnostic and therapeutic precision. The integration of artificial intelligence, wearable biosensors, telemedicine, and point-of-care imaging has transformed emergency medicine, enabling more efficient triage, real-time monitoring, and early intervention for critically ill patients. Additionally, innovations in robotic-assisted procedures, automated decision-support systems, and portable laboratory diagnostics are reshaping emergency and pre-hospital care.

This Special Issue aims to explore cutting-edge technologies that are revolutionizing emergency medical services (EMS), emergency departments, and critical care settings. We welcome original research and reviews that highlight the clinical application, effectiveness, and future prospects of novel technologies in emergency medicine.

Dr. Sangsoo Han
Prof. Dr. Young Soon Cho
Guest Editors

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Keywords

  • emergency medical services
  • artificial intelligence in emergency medicine
  • telemedicine and remote patient monitoring
  • wearable health technologies
  • point-of-care diagnostics
  • automated decision support systems
  • robotics in emergency medicine
  • pre-hospital care innovations

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Published Papers (1 paper)

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Research

12 pages, 2807 KiB  
Article
A Novel Artificial Intelligence-Based Mobile Application for Pediatric Weight Estimation
by Sungwoo Choi, Sangun Nah, Ji Eun Moon and Sangsoo Han
J. Clin. Med. 2025, 14(9), 2873; https://doi.org/10.3390/jcm14092873 - 22 Apr 2025
Viewed by 193
Abstract
Background/Objectives: Pediatric drug dosages are typically weight-based. Length-based weight estimation tools used in emergency situations require full body extension, which may cause measurement errors in restricted positions. In this study, we developed and evaluated a weight prediction application using MoveNet’s human pose [...] Read more.
Background/Objectives: Pediatric drug dosages are typically weight-based. Length-based weight estimation tools used in emergency situations require full body extension, which may cause measurement errors in restricted positions. In this study, we developed and evaluated a weight prediction application using MoveNet’s human pose estimation and a deep neural network (DNN) regression model. Methods: This prospective cross-sectional study was conducted from June 2023 to May 2024 and included pediatric patients aged 1 month to 12 years. Weight estimation accuracy was compared between the Pediatric Artificial Intelligence weight-estimating Camera (PAICam) and the Broselow tape (BT) using mean percentage error (MPE), mean absolute percentage error (MAPE), and root mean square percentage error (RMSPE). The percentages of weight estimations within 10% (PW10) and 20% (PW20) of the actual weights were calculated. Intraclass correlation coefficients (ICCs) were used to evaluate agreement between predicted and actual weights. Results: In total, 1335 pediatric participants were analyzed (57.4% boys, 42.6% girls), with an average age of 4 years. The BT and PAICam showed comparable performance, with similar values for MPE (−1.44% vs. 5.29%), MAPE (11.28% vs. 12.41%), and RMSPE (3.09% vs. 3.42%). PW10 and PW20 for the BT and PAICam were also similar (52.6% vs. 51.2% and 79.1% vs. 77.7%). ICC values demonstrated strong agreement between actual and predicted weights for both methods (0.959 vs. 0.955). Conclusions: PAICam, utilizing deep learning and human pose estimation technology, demonstrated performance and accuracy comparable to the BT. This suggests its potential as an alternative tool for pediatric weight estimation in emergency settings. Full article
(This article belongs to the Special Issue Novel Technologies to Assist Emergency Medical Care)
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