ArticleBMC medical education2024
Analysis of virtual standardized patients for assessing clinical fundamental skills of medical students: a prospective study.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study.JMIR medical education · 2026Article
- A Medical Large Language Model-Based System Improves History-Taking Performance Among Medical Students.Cureus · 2026Article
- A narrative discussion on the impact of respiratory infectious diseases on dental education in China: implications for curriculum design and student outcomes.Frontiers in dental medicine · 2026Review
- Development and Validation of a Large Language Model-Based System for Medical History-Taking Training: Prospective Multicase Study on Evaluation Stability, Human-AI Consistency, and Transparency.JMIR medical education · 2025Article
- Advances in the application of human-machine collaboration in healthcare: insights from China.Frontiers in public health · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHistory-taking is an essential clinical competency for qualified doctors. The limitations of the standardized patient (SP) in taking history can be addressed by the virtual standardized patient (VSP). This paper investigates the accuracy of virtual standardized patient simulators and evaluates the applicability of the improved system's accuracy for diagnostic teaching support and performance assessment.
methodsData from the application of VSP to medical residents and students were gathered for this prospective study. In a human-machine collaboration mode, students completed exams involving taking SP histories while VSP provided real-time scoring. Every participant had VSP and SP scores. Lastly, using the voice and text records as a guide, the technicians will adjust the system's intention recognition accuracy and speech recognition accuracy.
resultsThe research revealed significant differences in scoring across several iterations of VSP and SP (p < 0.001). Across various clinical cases, there were differences in application accuracy for different versions of VSP (p < 0.001). Among training groups, the diarrhea case showed significant differences in speech recognition accuracy (Z = -2.719, p = 0.007) and intent recognition accuracy (Z = -2.406, p = 0.016). Scoring and intent recognition accuracy improved significantly after system upgrades.
conclusionVSP has a comprehensive and detailed scoring system and demonstrates good scoring accuracy, which can be a valuable tool for history-taking training.
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Registered trials
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