Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with con-flicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping, to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from Scopus bib-liographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is re-vealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive biblio-metrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Biblio-metric mapping identified five key research themes: Game theory in cancer research, Evolution game-based simu-lation of supply management, Evolutionary game theory in epidemics, Evolutionary games in trustworthy con-nected public health, and Evolutionary games in collaborative governance. Conclusion: Despite EGT's utility, significant research gaps exist in methodological robustness, data availability, contextual modeling, and interdisciplinary translation. Future research should focus on integrating machine learn-ing, longitudinal data, and explicit ethical frameworks to enhance EGT's practical application in adaptive, pa-tient-centered healthcare systems.
Kokol et al. (Wed,) studied this question.