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Emergency Care and Medicine
Special Issues
Application of Artificial Intelligence in Emergency Care
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Application of Artificial Intelligence in Emergency Care

A special issue of Emergency Care and Medicine (ISSN 2813-7914).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2368

Special Issue Editor

Dr. Zhongheng Zhang

E-Mail Website
Guest Editor
Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
Interests: emergency and critical care medicine; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This collection of articles delves into the intersection of artificial intelligence (AI) and emergency medicine, with a particular focus on leveraging insights from transcriptomics, proteomics, and radiomics. From predictive analytics for risk stratification to individualized treatment strategies, these articles explore the multifaceted role of AI in enhancing patient care in emergency and critical care scenarios. By harnessing data from electronic health records, transcriptome analyses, proteomic profiling, and radiomic imaging, researchers are uncovering novel approaches to early detection, accurate diagnosis, and personalized treatment planning. Through innovative methods and advancements in AI-driven approaches, this Special Issue aims to showcase the transformative potential of AI in revolutionizing emergency care practices.

Dr. Zhongheng Zhang
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Emergency Care and Medicine is an international peer-reviewed open access quarterly 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 1000 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

  • artificial intelligence (AI)
  • emergency medicine
  • emergency care

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Published Papers (1 paper)

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Review

13 pages, 248 KiB  
Review
Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review
by Abdullah Basnawi and Ahmad Koshak
Emerg. Care Med. 2024, 1(3), 247-259; https://doi.org/10.3390/ecm1030026 - 17 Aug 2024
Viewed by 1882
Abstract
Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make [...] Read more.
Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty. Full article
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