Evidence map›Paper›PMID 41464253›Full record

ArticleHealthcare (Basel, Switzerland)2025

Can a Generative Artificial Intelligence Model Be Used to Create Mass Casualty Incident Simulation Scenarios? A Feasibility Study.

Sergio M Navarro, Angie G Atkinson, Ege Donagay, Maxwell Jabaay, Sarah Lund, Myung S Park, Erica A Loomis, John M Zietlow, T N Diem Vu, Mariela Rivera and 1 more

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In one paragraph

Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Sergio M NavarroDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.ORCID 0000-0003-3455-1567
Angie G AtkinsonDepartment of Surgery, The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA.
Ege DonagayDepartment of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey.
Maxwell JabaayDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
Sarah LundDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.ORCID 0000-0003-2214-5916
Myung S ParkDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
Erica A LoomisDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
John M ZietlowDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
T N Diem VuDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.ORCID 0000-0003-4405-3190
Mariela RiveraDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
Daniel StephensDivision of Trauma, Critical Care, and General Surgery, Department of Surgery, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.ORCID 0000-0002-6980-0001

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
Mayo Clinic StARR ProgramR38HL150086 · NHLBI · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC, KARL A. NATH · 2020 to 2026
$2.5M
NCATS NIH HHS UL1 TR002377NIH Stimulating Access to Research in Residency R38HL150086
6 · The paper itself

Abstract

introductionMass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool.

methodsUsing a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles.

resultsTen mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74-82), substantially lower than the median score of 94 (IQR 93-95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93-96) with an interrater reliability ICC of 0.7425.

conclusionsThe feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value.

Indexed as

generative artificial intelligencemultiple traumasimulation trainingtrauma centers

Identifiers

PMID41464253
PMCPMC12732669

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.