Effective 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. An 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. No 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. Integration 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.
Xie et al. (Tue,) studied this question.