Research Article: Virtual patients and standardized patients combined training is associated with improved clinical reasoning among medical students
Abstract:
To develop an artificial intelligence (AI)-driven virtual standardized patients (VSPs) system, and to evaluate its educational effectiveness when combined with traditional standardized patients (SPs) training.
Leveraging natural language processing and a Chinese large language model, we built an AI-powered VSPs application. A total of 80 medical students at Peking University Third Hospital were randomized into two groups: experimental ( n = 40, VSPs and SPs combined training) and control ( n = 40, traditional SPs training). The 4-week intervention included assessments on clinical reasoning, core competencies, OSCE performance, and learning experience.
The experimental group showed greater improvement than the control group in clinical reasoning scores (1.3 ± 0.7 vs. 0.3 ± 0.5, 95% CI: 0.73–1.27, P < 0.01), core competencies total scores (9.9 ± 4.1 vs. 3.8 ± 2.7, 95% CI: 4.56–7.64, P < 0.01), and OSCE performance (11.9 ± 5.2 vs. 3.2 ± 5.0, 95% CI: 6.43–10.97, P < 0.01). The improvements were observed in communication, patient care, and internal medicine knowledge. Learner-reported satisfaction was higher (4.6 ± 0.5 vs. 3.2 ± 0.8, 95% CI: 1.10–1.70, P < 0.01). Subgroup analysis revealed greater benefits for students with lower baseline scores.
Combined VSPs and SPs training showed better performance than the SPs-only approach used in this study.
Introduction:
Standardized patients (SPs) have been widely adopted in medical education, providing consistency and depth in skills training. However, traditional SPs-based training was costly, resource-intensive, and frequently lacks repeatability and uniformity over time ( 1 – 3 ). Recent advances in virtual reality (VR) and natural language processing (NLP) have facilitated the development of Virtual standardized patients (VSPs), which offer high consistency, repeatability, and scalability across diverse clinical scenarios (…
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