Enhancing objective structured clinical examination performance through an artificial intelligence virtual patient: a proof-of-concept study

BACKGROUND: The application of artificial intelligence (AI) in simulating detailed patient-doctor interactions for objective structured clinical examinations (OSCEs) remains emerging. This study aimed to evaluate an AI virtual patient (AIVP) innovation designed to support medical education through interactive patient simulations and feedback. METHOD: This prospective mixed-methods pilot recruited final-year medical students during their critical care term. Two cohorts were examined: a volunteer AIVP group (n = 43) and an educational-support medical education enhancement training (MEET) group (n = 8). Participants completed pre- and postintervention OSCEs scored across communication, history and examination, management, and overall outcome domains. Students accessed nine emergency medicine scenarios featuring AI-driven dialogue, speech recognition, avatar interaction, and immediate feedback. Quantitative outcomes were analyzed using the Wilcoxon signed-rank test. Postintervention surveys (Likert scale) and focus groups provided qualitative data, which were analyzed thematically. RESULTS: = 0.03), with positive trends in all domains. Survey responses (n = 25) showed high agreement that AIVP enhanced confidence in history-taking (83%), differential diagnosis (78%), and management planning (78%). Thematic analysis from 10 students demonstrated that AIVP was experienced as a structured, exam-focused, and confidence-building learning tool that addresses gaps in workplace-based learning. CONCLUSION: AIVP was feasible and well received, providing practice aligned with university OSCE standards. While the volunteer cohort showed modest gains, the MEET group demonstrated significant improvement, suggesting benefit for students needing structured support. Tailored feedback and alignment with OSCE standards highlight AIVP as a promising adjunct for training.