Evidence map›Paper›PMID 41264856›Full record

Trial reportJMIR formative research2025

Web-Based AI-Driven Virtual Patient Simulator Versus Actor-Based Simulation for Teaching Consultation Skills: Multicenter Randomized Crossover Study.

Edward G Tyrrell, Sardip K Sandhu, Kathryn Berry, Suzan F Ghannam, Sarah A Lewis, Daniel Crowfoot, Gurvinder Singh Sahota, Julie Carson, Emma E Wilson, Jaspal Taggar

Abstract readRandomized Controlled TrialMulticenter Study
In one paragraph

Trial report in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Strengths and Potential Pitfalls of the Use of Artificial Intelligence in Psychiatric Education and Practice.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
  4. Article
  5. 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

10 authors.

Edward G TyrrellPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0003-2171-6334
Sardip K SandhuPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0009-0001-0038-1742
Kathryn BerryLincoln Medical School, University of Lincoln, Lincoln, United Kingdom.ORCID 0009-0006-5211-2524
Suzan F GhannamPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0003-4171-2605
Sarah A LewisPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0001-5308-6619
Daniel CrowfootPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0001-8531-3361
Gurvinder Singh SahotaPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0001-8896-5234
Julie CarsonPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0009-0009-9962-8482
Emma E WilsonPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0002-4695-2184
Jaspal TaggarPrimary Care Education Unit, School of Medicine, University of Nottingham, Room C34, Queen's Medical Centre, Nottinghamshire, NG7 2UH, United Kingdom, 44 1158231418.ORCID 0000-0002-5031-0977

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There is a need to increase health care professional training capacity to meet global needs by 2030. Effective communication is essential for delivering safe and effective patient care. Artificial intelligence (AI) technologies may provide a solution. However, evidence for high-fidelity virtual patient simulators using unrestricted 2-way verbal conversation for communication skills training is lacking. Objective: This study aims to compare a fully automated AI-driven voice recognition-based virtual patient simulator with traditional actor-based consultation skills simulated training in undergraduate medical students for differences in developing self-rated communication skills, student satisfaction scores, and direct cost comparison. Methods: Using an open-label randomized crossover design, a single web-based AI-driven communication skills training session (AI-CST) was compared with a single face-to-face actor-based consultation skills training session (AB-CST) in undergraduates at 2 UK medical schools. Offline total cohort recruitment was used, with an opt-out option. Pre-post intervention surveys using 10-point linear scales were used to derive outcomes. The primary outcome was the difference in self-reported attainment of communication skills between interventions. Secondary outcomes were differences in student satisfaction and the cost comparison of delivering both interventions. Results: Of 396 students, 378 (95%) completed at least 1 survey. Both modalities significantly increased self-reported communication skills attainment (AI-CST: mean difference 1.14, 95% CI 0.97-1.32 points; AB-CST: mean difference 1.50, 95% CI 1.35-1.66 points; both P<.001). Attainment increase was lower for AI-CST than AB-CST (by mean difference 0.36, 95% CI -0.60 to -0.13 points; P=.04). Overall satisfaction was lower for AI-CST than AB-CST (8.09 vs 9.21; mean difference -1.13, 95% CI -1.33 to -0.92 for AI-CST vs AB-CST; P<.001). The estimated costs of AI-CST and AB-CST were £33.48 (US $42.22) and £61.75 (US $77.87) per student, respectively. Conclusions: AI-CST and AB-CST were both effective at improving self-reported communication skills attainment, but AI-CST was slightly inferior to AB-CST. Student satisfaction was significantly greater for AB-CST. Costs of AI-CST were substantially lower than AB-CST. AI-CST may provide a cost-effective opportunity to build training capacity for health care professionals.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduatePatient SimulationSimulation TrainingAdultCommunicationCross-Over StudiesFemaleHumansInternetMaleStudents, MedicalUnited Kingdomclinical educationcommunication skillsmedical educationsimulationtechnology enhanced learning

Identifiers

PMID41264856
PMCPMC12634008

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.