Actionable companion · Staff capability and AI literacy

From evidence to district action: Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Background and objective Virtual patients have become an integral component of medical education, offering medical students realistic, repeatable, and interactive learning environments that effectively foster the development of clinical competencies. Moreover, the rapid evolution of generative artificial intelligence and large language model technologies has further accelerated the integration of virtual patients into medical education, making it a prominent area of contemporary educational innovation. To provide a comprehensive overview of the research landscape and emerging trends in this domain, we performed a bibliometric and knowledge-mapping analysis of the literature on virtual patients in medical education. Methods Publications published between 2006 and 2025 were retrieved from the Web of Science Core Collection and Scopus databases, including only English articles and review articles. CiteSpace and VOSviewer were used to perform bibliometric and knowledge-mapping analyses of collaboration networks, co-citation patterns, and keyword evolution. Result A total of 2,347 publications showed a consistent upward trend. The United States led contributions, followed by the United Kingdom and Germany, with extensive collaborations between North America and Europe. Medical Education was the most influential journal; Karolinska Institutet was the most productive institution and Cook, DA. was the most prolific author. Frequent keywords included “medical education,” “simulation,” “clinical competence,” “virtual reality,” and “patient simulation.” The most cited article confirmed virtual patients outperform no intervention, providing an evidence base for their adoption in medical education. Conclusions This bibliometric analysis highlights the sustained growth and evolving research landscape of virtual patients in medical education. The findings indicate that recent research has increasingly focused on the integration of generative artificial intelligence and large language models into virtual patient systems. These findings provide a quantitative evidence base for future research priorities and the continued development of virtual patient-based medical education.

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Yinglan Ye; Hairong Tao; Siqi Huang; Meixia Zou; Yiming Chen; Peifeng Shen; Man Hao; Chunlong Liu. Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases. Frontiers in Medicine (2026-07-30). https://doi.org/10.3389/fmed.2026.1904001

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.