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Novel Technologies to Assist Emergency Medical Care
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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: 20 December 2026 | Viewed by 2860

Special Issue Editors

Dr. Sangsoo Han

E-Mail Website
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
Prof. Dr. Young Soon Cho

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (3 papers)

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Research

14 pages, 590 KB  
Article
Complementary Error Patterns Between Human Evaluators and GPT-4o in Video-Based Cardiopulmonary Resuscitation Skills Assessment: Implications for Artificial Intelligence-Assisted Second Reading
by Hye Ji Park, Daun Choi and Choung Ah Lee
J. Clin. Med. 2026, 15(12), 4436; https://doi.org/10.3390/jcm15124436 - 8 Jun 2026
Viewed by 107
Abstract
Background/Objectives: Cardiopulmonary resuscitation (CPR) skill assessments are susceptible to evaluator subjectivity, cognitive fatigue, and observational limitations. Although recent advances in multimodal artificial intelligence have increased the possibility of automated video-based assessment, its validity for clinical skill evaluation remains insufficiently examined. Methods: In [...] Read more.
Background/Objectives: Cardiopulmonary resuscitation (CPR) skill assessments are susceptible to evaluator subjectivity, cognitive fatigue, and observational limitations. Although recent advances in multimodal artificial intelligence have increased the possibility of automated video-based assessment, its validity for clinical skill evaluation remains insufficiently examined. Methods: In this cross-sectional study, we enrolled 130 laypersons who underwent Basic Life Support training and skill testing. Twenty recordings were used for prompt development and 110 recordings were analyzed. Expert evaluators and GPT-4o independently assessed participants’ skills using a 12-item checklist. The manikin sensor data were the reference standard for the four chest compression metrics. Agreement was evaluated using Gwet’s agreement coefficient 1 (AC1) and intraclass correlation coefficient (2,1). Diagnostic accuracy, sensitivity, and specificity were compared using McNemar’s test. Results: Procedural items such as confirming cardiac arrest, calling 119, and requesting an automated external defibrillator showed a near-perfect agreement between experts and GPT-4o (AC1 > 0.8). However, the agreement was poor for the compression depth (AC1 = 0.374) and full chest recoil (AC1 = 0.355). Experts demonstrated high sensitivity (77.8–84.3%) but low specificity (24.6–47.8%), whereas GPT-4o showed low sensitivity (35.6–40.6%) but high specificity (69.2–76.1%). Conclusions: GPT-4o cannot serve as a standalone evaluator because of its inherent limitations in inferring three-dimensional spatial information from two-dimensional videos. However, its high agreement on procedural items and complementary error patterns with that of human evaluators on compression metrics suggests its potential as a decision support tool to mitigate expert leniency bias in CPR education. Full article
(This article belongs to the Special Issue Novel Technologies to Assist Emergency Medical Care)
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13 pages, 711 KB  
Article
The Potential Role of Large Language Models in Assisting Patients and Guiding Emergency Care Visits
by Kristina Gerhardinger, Josina Straub, Julia Lenz, Siegmund Lang, Volker Alt, Borys Frankewycz, Maximilian Kerschbaum and Lisa Klute
J. Clin. Med. 2026, 15(8), 3170; https://doi.org/10.3390/jcm15083170 - 21 Apr 2026
Viewed by 531
Abstract
Background/Objectives: Overcrowding in emergency departments (EDs) remains a critical challenge in modern healthcare systems, driven in part by patient uncertainty regarding symptom urgency and a lack of accessible medical guidance. Recent advances in artificial intelligence, particularly large language models (LLMs), present a [...] Read more.
Background/Objectives: Overcrowding in emergency departments (EDs) remains a critical challenge in modern healthcare systems, driven in part by patient uncertainty regarding symptom urgency and a lack of accessible medical guidance. Recent advances in artificial intelligence, particularly large language models (LLMs), present a novel opportunity to support patient navigation and relieve pressure on ED infrastructures. Methods: A total of 238 unique patient questions were identified through a structured web search. Following deduplication and thematic clustering, 15 representative questions were selected. Each question was submitted to the three LLMs—ChatGPT (v3.5), DeepSeek, and Gemini—using a standardized prompt. Responses were assessed by clinical experts (N = 8) who were blinded to the model source. Reviewers selected the best overall response per question, as well as the individual responses of the three LLMs for each respective question. Results: ChatGPT was selected as the best-performing model in 60% of cases, with DeepSeek and Gemini selected in 23% and 17%, respectively. ChatGPT responses also achieved the highest proportion of “excellent” quality ratings and the lowest proportion of “unsatisfactory” outputs. Across all models, clarity was the most positively rated domain (79% agreement), followed by empathy (72%), length/detail appropriateness (71%), and completeness (65%). Over two-thirds of raters expressed willingness to integrate LLM-based tools into clinical practice for patient education and pre-triage counseling. Conclusions: Large language models demonstrate promising capabilities in responding to emergency care-related patient queries. Their ability to deliver medically sound and communicatively effective answers positions them as potential digital adjuncts in the management of low-acuity ED presentations and prehospital triage. Full article
(This article belongs to the Special Issue Novel Technologies to Assist Emergency Medical Care)
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12 pages, 2807 KB  
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
Cited by 1 | Viewed by 1739
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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