Evidence map›Paper›PMID 39609823›Full record

Trial reportBMC medical education2024

Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial.

Emilia Brügge, Sarah Ricchizzi, Malin Arenbeck, Marius Niklas Keller, Lina Schur, Walter Stummer, Markus Holling, Max Hao Lu, Dogus Darici

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 6 pooled it
–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

67 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  14. Examining the Role of Artificial Intelligence in Assessment: A Comparative Study of ChatGPT and Educator-Generated Multiple-Choice Questions in a Dental Exam.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
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7 more citing papers are in PubMed but not listed here.

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

9 authors.

Emilia Brügge *Connectome - Student Association for Neurosurgery, Neurology and Neurosciences, Berlin, Germany.
Sarah Ricchizzi *Connectome - Student Association for Neurosurgery, Neurology and Neurosciences, Berlin, Germany.
Malin ArenbeckConnectome - Student Association for Neurosurgery, Neurology and Neurosciences, Berlin, Germany.
Marius Niklas KellerConnectome - Student Association for Neurosurgery, Neurology and Neurosciences, Berlin, Germany.
Lina SchurConnectome - Student Association for Neurosurgery, Neurology and Neurosciences, Berlin, Germany.
Walter StummerDepartment of Neurosurgery, University Hospital of Münster, Münster, Germany.
Markus HollingDepartment of Neurosurgery, University Hospital of Münster, Münster, Germany.
Max Hao LuHarvard Graduate School of Education, Cambridge, USA.
Dogus DariciInstitute of Anatomy and Neurobiology, University of Münster, Vesaliusweg 2-4, 48149, Münster, Germany. darici@uni-muenster.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision-making (CDM) refers to physicians' ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback.

methodsWe developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator - History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures.

resultsOur final sample included 21 medical students (age

conclusionThe results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations.

Indexed as

Clinical Decision-MakingPatient SimulationStudents, MedicalAdultArtificial IntelligenceClinical CompetenceDouble-Blind MethodEducation, Medical, UndergraduateFeedbackFemaleFormative FeedbackHumansMaleMedical History TakingPhysician-Patient RelationsYoung AdultClinical decision makingLarge language modelsMedical students educationPatient simulation trainingStructured feedback

Identifiers

PMID39609823
PMCPMC11605890

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.