AI-driven virtual standardized patients combined with scenario-based simulation in anesthesiology training: a randomized pilot study.

Background: Anesthesiology trainees require repeated opportunities to practice perioperative crisis recognition, decision-making, communication and teamwork, which are central to crisis resource management (CRM) and non-technical skills. High-fidelity simulation supports these competencies but is resource intensive to deliver repeatedly. AI-driven virtual standardized patients (AI-VSPs) may complement simulation and structured debriefing by enabling interactive practice and feedback. Methods: We conducted a prospective, randomized, three-arm pilot study among 60 students enrolled in a professional master's degree program in anesthesiology at Zhongnan Hospital of Wuhan University between January and June 2026. Participants were assigned to conventional teaching (group A, Results: All participants completed the study and were analyzed. No statistically significant between-group differences were detected in the reported baseline characteristics. In participant-level post-intervention analyses, total simulated clinical performance was higher in group C than in groups A and B (88.2 ± 4.8 vs. 78.0 ± 7.7 and 80.4 ± 6.5; F(2, 57) = 13.72, Conclusions: A time-matched pathway in which AI-VSP practice replaced part of conventional case discussion while simulation and structured debriefing were retained was associated with better short-term performance on aligned anesthesiology crisis tasks. These findings support the feasibility of this approach and suggest potential benefits in selected CRM-relevant domains, but do not establish comprehensive CRM competence, definitive effectiveness or durable transfer to clinical practice. Larger multicenter studies with prespecified analyses, detailed reporting of AI-system characteristics, delayed assessment and workplace-based outcomes are needed.