Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases
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.