Evidence map›Paper›PMID 41654983›Full record

ArticleWorld journal of emergency surgery : WJES2026

Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.

Belinda De Simone, Lucienne Kasongo, Andrew A Gumbs, Fabrizio Vecchio, Alberto De Franceschi, Nicola DèAngelis, Andrew W Kirkpatrick, Juan P Wachs, Tyler J Loftus, Fikri M Abu-Zidan and 16 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of emergency surgery : WJES, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

26 authors.

Belinda De SimoneDepartment of Emergency and General Minimally Invasive Surgery, Bufalini Hospital, AUSL Romagna, Via Ghirotti 286, 47521, Cesena, Italy. desimone.belinda@gmail.com.
Lucienne KasongoGeneral Hospital of Kinshasa, Avenue de L'hopital, Kinshasa, Democratic Republic of Congo. ariesproject2023@gmail.com.
Andrew A GumbsDepartment of Minimally Invasive Viscerale and Digestive Surgery, Hôpital Antoine Béclère, AP-HP, Université Paris-Saclay, Clamart, France.
Fabrizio VecchioBrain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.
Alberto De FranceschiDepartment of Law, University of Ferrara, Ferrara, Italy.
Nicola DèAngelisDepartment of Surgery, Fondazione Poliambulanza Instituto Ospedaliero, Brescia, Italy.
Andrew W KirkpatrickFoothills Medical Centre, Alberta Health Services, 1403 29 St NW, Calgary, AB, T2N 2T9, Canada.
Juan P WachsGerald D. and Edna E. Mann Hall, Regenstrief Center for Healthcare Engineering, Suite 225, 203 S. Martin Jischke Drive, West Lafayette, IN, 47907-1971, USA.
Tyler J LoftusDepartment of Surgery, University of Florida Health, Gainesville, FL, USA.
Fikri M Abu-ZidanDepartment of Surgery, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
Rifat LatifiThe University of Arizona, Tucson, AZ, USA.
Genevieve DeekenCentre de Recherche en Epidemiologie Et Statistiques (CRESS), Sorbonne Paris Cité University, 75006, Paris, France.
Elie ChouillardDepartment of General and Bariatric Surgery, American Hospital of Paris, 92200, Neuilly-Sur-Seine, France.
Andrey LitvinDepartment of Surgical Diseases No. 3, Gomel State Medical University, University Clinic, Gomel, Belarus.
Massimo SartelliDepartment of General Surgery, Macerata Hospital, Macerata, Italy.
Desiree PantaloneDepartment of Experimental and Clinical Medicine, University of Florence, 50134, Florence, Italy.
Ari LeppäniemiAbdominal Center, Department of Abdominal Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Mehmet EryilmazDepartment of Surgery, Gülhane Medical Faculty, Gülhane Education & Training Hospital, 06010, Ankara, Turkey.
Kemal RasaDepartment of Surgery, Anadolu Medical Center, Kocaali, Turkey.
Arda IsikIstanbul Medeniyet University, Istanbul, Turkey.
Haytham M KaafaraniDepartment of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Gustavo FragaDivision of Trauma Surgery, School of Medical Sciences, University of Campinas, Campinas, Brazil.
Raul CoimbraRiverside University Health System Medical Center, Riverside, CA, USA.
Ernest E MooreErnest E Moore Shock Trauma Center at Denver Health, University of Colorado, Denver, CO, USA.
Walter L BifflDivision of Trauma/Acute Care Surgery, Scripps Clinic Medical Group, La Jolla, San Diego, CA, USA.
Fausto CatenaDepartment of Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40126, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo map and critically appraise the current literature on Artificial Intelligence (AI) applications in emergency general surgery, with a focus on clinical decision-support tools for preoperative risk stratification and intraoperative assistance, and to identify ethical, structural, and regulatory barriers to implementation.

methodsA scoping review was conducted within the ARIES project, following established methodological frameworks. Relevant studies evaluating AI-based tools in emergency surgical settings were systematically identified and analyzed.

resultsThe literature describes AI applications mainly in two domains: preoperative decision support, including risk prediction and diagnostic or triage models for acute abdominal and traumatic conditions, and intraoperative assistance, largely focused on computer vision-based systems for anatomical recognition, safety guidance, and navigation in minimally invasive emergency procedures. Additional contributions address training and telementoring platforms, as well as cross-cutting ethical, legal, and regulatory considerations relevant to AI adoption in emergency surgical care.

conclusionsAI has the potential to complement emergency surgeons' clinical judgment, but its routine adoption in emergency surgical practice remains limited. Addressing methodological, ethical, and regulatory challenges, together with the development of robust data infrastructures and targeted training pathways, is essential to support safe, effective, and equitable implementation in acute care settings. In addition, the lack of dedicated investment and sustainable funding models for large-scale clinical implementation and prospective evaluation represents a critical barrier to the translation of AI from research into routine emergency surgical practice.

Indexed as

Artificial IntelligenceAcute Care SurgeryHumansAccountabilityArtificial IntelligenceCognitive LoadComputer VisionDecision-MakingDeep LearningDigital SurgeryEducationEmergency SurgeryEthicsFluorescence ImagingMachine LearningReliabilityRobotic surgeryTechnologyTraining

Identifiers

PMID41654983
PMCPMC12977898

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.