Trial reportBMC medical education2026
An exploratory study of the use of artificial intelligence-based virtual patients to enhance dentist-patient communication training.
Trial report 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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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.
The trial behind it
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Who cites it
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEffective doctor-patient communication is critical in dentistry for diagnostic accuracy and treatment efficacy. Traditional instructional formats afford limited practice opportunities, impeding the transfer of theoretical knowledge to clinical settings. Standardised patients (SPs) provide authentic interaction but are costly and logistically demanding, restricting training scalability. Large language models (LLMs), capable of generating contextually adaptive dialogues, offer innovative opportunities for dental communication training.
methodsAn AI agent was developed using the DeepSeek large language model. Thirty-eight fourth-year dental students were randomly assigned to an experimental group (theoretical instruction plus AI-based virtual patient consultations) or a control group (theoretical instruction plus peer-to-peer role-play practice). Baseline and post-intervention doctor-patient communication skills were assessed using standardised patients consultations scored with the Set Elicit Give Understand End (SEGUE) scale. Post-intervention questionnaires assessed AI agent usability and participant satisfaction.
resultsNo statistically significant between-group difference was observed at baseline (p > 0.05). Following the intervention, the experimental group's SP consultation scores were significantly higher than those of the control group (p < 0.001), with particularly pronounced gains in the preparation stage and consultation closure. Questionnaire data indicated high levels of participant satisfaction and acceptance.
conclusionsIntegration of theoretical instruction with AI agent-based training demonstrates preliminary efficacy in improving dental students' doctor-patient communication skills and shows promise as a cost-efficient supplement to conventional training. Current limitations in dialogue flexibility and emotional intelligence should be addressed in future iterations.
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