Evidence map›Paper›PMID 40034198›Full record

ArticleJournal of the American College of Emergency Physicians open2025

Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.

Moira E Smith, C Christopher Zalesky, Sangil Lee, Michael Gottlieb, Srikar Adhikari, Mat Goebel, Martin Wegman, Nidhi Garg, Samuel H F Lam

Abstract read
In one paragraph

Article in Journal of the American College of Emergency Physicians open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  6. Multi-method approach to the development of contextual telephone triage systems.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
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  7. Review
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  9. Article
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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

9 authors.

Moira E SmithDepartment of Emergency Medicine, University of Virginia, Charlottesville, Virginia, USA.
C Christopher ZaleskyDepartment of Anesthesia, Division of Critical Care, Emory University School of Medicine, Atlanta, Georgia, USA.
Sangil LeeDepartment of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.
Michael GottliebEmergency Ultrasound Division, Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois, USA.
Srikar AdhikariDepartment of Emergency Medicine, University of Arizona, Tucson, Arizona, USA.
Mat GoebelDepartment of Emergency Medicine, Mercy Medical Center - Trinity Health of New England, Springfield, Massachusetts, USA.
Martin WegmanDepartment of Emergency Medicine, Orange Park Medical Center, Orange Park, Florida, USA.
Nidhi GargDepartment of Emergency Medicine, South Shore University Hospital/Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA.
Samuel H F LamSection of Emergency Medicine, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, USA.

Funding

Automated Sonographic Detection of Pulmonary Embolism Using Machine Learning AlgorithmR21EB030677 · NIBIB · UNIVERSITY OF ARIZONA · PI ADHIKARI, SRIKAR REDDY · 2023 to 2024
$606k
NIBIB NIH HHS R21 EB030677
6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly being utilized to augment the practice of emergency medicine due to rapid technological advances and breakthroughs. AI applications have been used to enhance triage systems, predict disease-specific risk, estimate staffing needs, forecast patient decompensation, and interpret imaging findings in the emergency department setting. This article aims to help readers without formal training become informed end-users of AI in emergency medicine. The authors will briefly discuss the principles and key terminology of AI, the reasons for its rising popularity, its potential applications in the emergency department setting, and its limitations. Additionally, resources for further self-studying will also be provided.

Indexed as

artificial intelligenceeducationinformaticsmachine learningnatural language processingneural networks

Identifiers

PMID40034198
PMCPMC11874537

What OpenQuestion 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 OpenQuestion graph.