Evidence map›Paper›PMID 41249898›Full record

ReviewInternational journal of emergency medicine2025

Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.

Hanieh Alimiri Dehbaghi, Karim Khoshgard

Abstract readReview
In one paragraph

Review in International journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hanieh Alimiri DehbaghiDepartment of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0004-7410-669X
Karim KhoshgardDepartment of Medical Physics, School of Medicine, Kermanshah University of Medical Sciences, Sorkheh-Lizhe Blvd, P.O Box:1568, Kermanshah, Iran. khoshgardk@gmail.com.ORCID http://orcid.org/0000-0003-3629-3389

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe deployment of artificial intelligence (AI) applications in the healthcare domain has witnessed a significant and noteworthy surge. This is particularly pronounced within the fast-paced and critical realm of emergency care, where the integration of AI has manifested as a transformative force, exerting profound influence on the diagnosis of trauma-related complications.

objectiveThis scholarly article aims to provide an in-depth exploration of the multifaceted applications of AI in the emergency department, elucidating its remarkable efficacy in expediting and refining the precision of diagnoses and patient management within this exigent setting.

methodsThrough a meticulous and comprehensive review of pertinent literature, this study endeavors to delineate and emphasize key AI applications, thereby illuminating their significant impact in optimizing patient outcomes and rationalizing workflows within emergency care. This scholarly exploration seeks to underscore the burgeoning potential of AI as an indispensable ally in the collective pursuit of achieving apid and accurate diagnoses, particularly in high-stakes emergency settings.

resultsFindings reveal that AI is driving a paradigm shift in emergency medicine by transforming clinical approaches to urgent cases. Its implementation has shown substantial potential in optimizing patient outcomes and streamlining clinical workflows.

conclusionAI stands as a promising and indispensable tool in the pursuit of rapid and accurate diagnoses in emergency care. Its continued integration is poised to significantly enhance clinical decision-making and patient care in high-stakes scenarios.

Indexed as

Artificial intelligenceDeep learningEmergency careMachine learningTrauma

Identifiers

PMID41249898
PMCPMC12621375

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.