JMIR Publications

ABSTRACT

Background:

Clinical decision-making training in operative dentistry commonly relies on real or standardized patients to develop undergraduate students’ ability to deliver safe, effective, and patient-centered care. However, training with real or standardized patients can be limited in scalability, cost-effectiveness, and accessibility. Large language models, with their human-like language capabilities, may have the potential to simulate patients in clinical encounters and help overcome some limitations associated with traditional training approaches.

Objective:

This study aimed to evaluate the feasibility of using large language model–based standardized virtual patients to support undergraduate dental students’ clinical decision-making training in operative dentistry.

Methods:

This cohort feasibility study involved 41 second-year undergraduate dental students and was conducted during a simulation-based clinical decision-making training session in operative dentistry. The students were divided into eight groups. Each group interacted with two standardized virtual patients powered by ChatGPT-4o (OpenAI) to complete comprehensive history taking, then reviewed the standardized virtual patients’ intraoral photographs and bitewing radiographs. For each standardized virtual patient, students as a group recorded diagnoses, performed a risk assessment and formulated a treatment plan. Students then completed the Student Satisfaction and Self-Confidence in Learning questionnaire. The quality of the standardized virtual patient responses and overall dialogue realism were evaluated using the Dialogue Authenticity Scale. The dialogues were also thematically analyzed to identify authenticity-undermining response features and to explore their context and underlying causes.

Results:

Students perceived the simulation-based training session positively, as all questionnaire items achieved high median scores (4.00–5.00 on a 5-point scale). In addition, standardized virtual patient responses were largely authentic, with an overall median authenticity rating of 4.50/6.00 across all interactions. However, several authenticity-undermining response features were identified, including responses that were inconsistent with typical human behavior, included information beyond a patient’s likely knowledge, or were factually incorrect.

Conclusions:

This proof-of-concept study supports the feasibility of implementing large language model–based standardized virtual patients in undergraduate simulation-based clinical decision-making training in operative dentistry. Further research is warranted to optimize performance and to evaluate educational effectiveness in improving undergraduate students’ clinical skills and knowledge. Clinical Trial: N/A