Evidence map›Paper›PMID 42351097›Full record

ArticleBMC medical education2026

Development and pilot evaluation of a large language model-based clinical simulation system for medical education in gastroenterology.

Stiliano Maimaris, Gianmarco Tedino, Giusi Aurora Memoli, Carlotta Crisciotti, Alessandra Albrizio, Camilla Sammartino, Erica Bartolotta, Giulio Massetti, Giulia Mantica, Paolo Minerba and 2 more

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

12 authors.

Stiliano MaimarisDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy. stiliano.maimaris01@universitadipavia.it.
Gianmarco TedinoDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Giusi Aurora MemoliDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Carlotta CrisciottiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Alessandra AlbrizioDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Camilla SammartinoDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Erica BartolottaDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Giulio MassettiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Giulia ManticaDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Paolo MinerbaDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Federico BiagiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.
Annalisa SchiepattiDepartment of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical education relies on experience to develop clinical reasoning skills, yet patient access is often limited. Large language models (LLMs) offer an accessible alternative for simulating patient encounters. We aimed to develop and evaluate an LLM-based clinical simulation system for gastroenterology that simulates entire clinical encounters in natural language, from history-taking through to treatment and follow-up planning.

methodsThe study was conducted in three phases. First, a system comprising an LLM-based clinical case generator, an LLM-based patient simulator, and a web chat interface was developed and iteratively refined. Second, two gastroenterology fellows performed 20 clinical case simulations each as initial testers, providing feedback that guided further system adjustments. Third, six independent gastroenterology fellows, blinded to the development process, evaluated the final system by completing the same 20 clinical simulations. All evaluators rated the system using a 5-point Likert scale across four domains: case presentation accuracy, adaptability to user interactions, realism, and educational value.

resultsA total of 160 evaluations were collected across 20 cases and 8 raters. At final evaluation, all domains scored highly: educational value (mean 4.57 ± 0.63), accuracy (4.42 ± 0.63), adaptability (4.33 ± 0.75), and realism (4.32 ± 0.81). Scores differed significantly across domains (p < 0.01), with educational value rated highest. Evaluation panellists rated all domains significantly higher than initial testers (all p < 0.01) at an exploratory comparison.

conclusionsThe LLM-based clinical simulator demonstrated high accuracy, realism, adaptability, and educational value for gastroenterology case simulation in this pilot evaluation, and may represent an accessible and cost-effective tool for clinical reasoning training.

Indexed as

Clinical CompetenceEducation, MedicalGastroenterologyLarge Language ModelsPatient SimulationSimulation TrainingHumansPilot ProjectsArtificial intelligenceClinical reasoningClinical simulationLarge language modelsMedical educationVirtual patient

Identifiers

PMID42351097
PMCPMC13556072

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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.