Evidence map›Paper›PMID 40320548›Full record

ArticleAdvances in simulation (London, England)2025

AI and inclusion in simulation education and leadership: a global cross-sectional evaluation of diversity.

Joana Berger-Estilita, Mia Gisselbaek, Arnout Devos, Albert Chan, Pier Luigi Ingrassia, Basak Ceyda Meco, Odmara L Barreto Chang, Georges L Savoldelli, Francisco Maio Matos, Peter Dieckmann and 2 more

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In one paragraph

Article in Advances in simulation (London, England), 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. Article
  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

12 authors.

Joana Berger-Estilita *Institute for Medical Education, University of Bern, Bern, Switzerland.
Mia Gisselbaek *Division of Anesthesiology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland. Mia.gisselbaek@gmail.com.
Arnout DevosETH AI Center, Swiss Federal Institute of Technology Zurich (ETH Zurich), Zurich, Switzerland.
Albert ChanDepartment of Anaesthesia, Pain and Perioperative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Pier Luigi IngrassiaCentro di Simulazione (CeSi), Centro Professionale Sociosanitario Medico-Tecnico, Lugano, Switzerland.
Basak Ceyda MecoDepartment of Anesthesia and Intensive Care, , Ankara University Faculty of Medicine, Ankara, Turkey.
Odmara L Barreto ChangDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA.
Georges L SavoldelliDivision of Anesthesiology, Department of Anesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
Francisco Maio MatosAnesthesiology Department, Hospitais da Universidade de Coimbra, Coimbra, Portugal.
Peter DieckmannCopenhagen Academy for Medical Education and Simulation (CAMES), Herlev, Copenhagen, Capital Region of Denmark, Denmark.
Doris ØstergaardCopenhagen Academy for Medical Education and Simulation (CAMES), Herlev, Copenhagen, Capital Region of Denmark, Denmark.
Sarah SaxenaDepartment of Anesthesiology, Helora, Mons, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimulation-based medical education (SBME) is a critical training tool in healthcare, shaping learners' skills, professional identities, and inclusivity. Leadership demographics in SBME, including age, gender, race/ethnicity, and medical specialties, influence program design and learner outcomes. Artificial intelligence (AI) platforms increasingly generate demographic data, but their biases may perpetuate inequities in representation. This study evaluated the demographic profiles of simulation instructors and heads of simulation labs generated by three AI platforms-ChatGPT, Gemini, and Claude-across nine global locations.

methodsA global cross-sectional study was conducted over 5 days (November 2024). Standardized English prompts were used to generate demographic profiles of simulation instructors and heads of simulation labs from ChatGPT, Gemini, and Claude. Outputs included age, gender, race/ethnicity, and medical specialty data for 2014 instructors and 1880 lab heads. Statistical analyses included ANOVA for continuous variables and chi-square tests for categorical data, with Bonferroni corrections for multiple comparisons: P significant < 0.05.

resultsSignificant demographic differences were observed among AI platforms. Claude profiles depicted older heads of simulation labs (mean: 57 years) compared to instructors (mean: 41 years), while ChatGPT and Gemini showed smaller age gaps. Gender representation varied, with ChatGPT and Gemini generating balanced profiles, while Claude showed a male predominance (63.5%) among lab heads. ChatGPT and Gemini outputs reflected greater racial diversity, with up to 24.4% Black and 20.6% Hispanic/Latin representation, while Claude predominantly featured White profiles (47.8%). Specialty preferences also differed, with Claude favoring anesthesiology and surgery, whereas ChatGPT and Gemini offered broader interdisciplinary representation.

conclusionsAI-generated demographic profiles of SBME leadership reveal biases that may reinforce inequities in healthcare education. ChatGPT and Gemini demonstrated broader diversity in age, gender, and race, while Claude skewed towards older, White, and male profiles, particularly for leadership roles. Addressing these biases through ethical AI development, enhanced AI literacy, and promoting diverse leadership in SBME are essential to fostering equitable and inclusive training environments.

trial registrationNot applicable. This study exclusively used AI-generated synthetic data.

Indexed as

Artificial intelligence (AI)BiasDiversity and inclusionEthical AI designGender representationHealthcare educationSimulation-based medical education

Identifiers

PMID40320548
PMCPMC12049791

What OpenQuestion holds

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LicenceCC BY
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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.