Evidence map›Paper›PMID 42094752›Full record

ReviewTrauma surgery & acute care open2026

Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.

Emma Gilman Burke, Chloe Nobuhara, Alex H Lee, Joshua Aaron Villarreal, Caitlin Anne Fitzgerald, Ryan Peter Dumas

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trauma surgery & acute care open, 2026. 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. TCCACS and MDR 2026 introduction.Trauma surgery & acute care open · 2026
    Article
  2. Article
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

6 authors.

Emma Gilman BurkeMichael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA.ORCID https://orcid.org/0009-0007-4872-0006
Chloe NobuharaDepartment of Surgery, Stanford University, Palo Alto, California, USA.
Alex H LeeDepartment of Surgery, Stanford University, Palo Alto, California, USA.
Joshua Aaron VillarrealDepartment of Surgery, Stanford University, Palo Alto, California, USA.ORCID https://orcid.org/0000-0001-6380-4880
Caitlin Anne FitzgeraldMichael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA.ORCID https://orcid.org/0009-0006-8178-5285
Ryan Peter DumasMichael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-6566-1833

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The first hour after injury, often termed the 'golden hour,' relies on rapid and accurate decision making to ensure timely delivery of trauma care. Coordination of prehospital resources, triage, resuscitation, and imaging is critical to patient outcomes. Unlike static algorithms or prediction scores, artificial intelligence (AI) models can integrate large volumes of data to provide actionable decision support. This narrative review explores current and emerging applications of AI in trauma care, specifically focusing on its role within the trauma bay.

Indexed as

Diagnostic imagingEmergency Medical ServicesMultiple Traumatriage

Identifiers

PMID42094752
PMCPMC13140952

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.