The state-of-the-art healthcare metaverse: A bibliometric analysis and an examination of healthcare metaverse acceptance in medical education and scheduling strategies

Abstract

The metaverse, once a concept confined to science fiction, has evolved into a technological paradigm that integrates virtual, augmented, mixed, and extended reality. These immersive tools have increasingly entered healthcare and medical education, enabling new approaches to clinical training, remote therapy, surgical simulation, and collaborative learning. The COVID-19 pandemic accelerated this trend by disrupting traditional education and highlighting the need for scalable and digitally accessible environments. Nevertheless, the academic literature on the healthcare metaverse remains fragmented and methodologically limited. This thesis addresses these gaps by investigating the adoption and implementation of metaverse technologies in healthcare, with a particular focus on medical education. A multi layered methodological approach is adopted, combining bibliometric mapping, behavioral modeling, and mathematical optimization to provide both theoretical and practical insights. Chapter 2 presents a bibliometric analysis of 3,721 publications indexed in the Web of Science between 1994 and 2022. The studies are categorized into thematic groups, and productivity patterns are assessed through Zipf's, Lotka's, and Bradford's laws. The findings highlight influential authors, countries, institutions, and underexplored themes, establishing a solid foundation for subsequent analyses. Chapter 3 examines the behavioral intentions of medical doctors in Turkey using the Technology Acceptance Model extended with technology anxiety. Structural Equation Modeling validates the framework and reveals that perceived usefulness, ease of use, and anxiety significantly affect adoption. Chapter 4 expands the model to include both practitioners and medical students. By integrating complementary theories such as Innovation Diffusion Theory, the Interaction Equivalence Theorem, and Embodied Social Presence Theory, the study demonstrates that satisfaction, presence, interaction, trialability, compatibility, readiness, and imagination all play important roles in acceptance. Chapter 5 develops a novel course scheduling optimization model to integrate metaverse based courses into medical curricula. The model incorporates professor preferences derived from Structural Equation Modeling and prioritized with the Analytic Hierarchy Process. A binary integer programming formulation is solved using Greedy and Simulated Annealing algorithms, showing that the latter achieves more balanced workloads and higher instructor satisfaction. The thesis concludes by emphasizing its theoretical contribution by broadening acceptance models with psychological and sociotechnical factors and its practical contribution by offering a replicable framework for institutions modernizing curricula through immersive technologies. Limitations include reliance on synthetic optimization data and the need for real world validation. Future studies should focus on longitudinal, cross cultural, and adaptive approaches. Overall, the thesis provides an integrative perspective on the role of the metaverse in healthcare education by uniting bibliometric, behavioral, and optimization methods, thereby enriching the literature and offering actionable guidance for decision makers.