Bridging the Knowledge Void: A Synthetic Near-Empty Review of Intelligent Evolutionary Games Employment in Healthcare

Background: The convergence of Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) has established the field of Intelligent Evolutionary Games (IEG). While IEG applications have flourished in general systems and social sciences, their operationalization within healthcare (IEG Health) remains significantly underdeveloped. This study identifies a "knowledge void" in the literature, where the bottleneck is not a lack of clinical data but a scarcity of frameworks that integrate intelligent strategic modeling into clinical practice. Methods: We employ the Synthetic Near-Empty Review (SNER) framework, utilizing Synthetic Knowledge Synthesis (SKS) and bibliometric triangulation via VOSviewer. Three distinct corpora—IEG Health (), EG Health (), and IEG All ()—were harvested from Scopus and mapped to identify thematic clusters and translation pathways. Results: The analysis reveals that IEG Health is a nascent domain currently focused on service regulation in elderly care and chronic disease management. We demonstrate a "Translation Framework" to bridge the research void, mapping concepts like Social Trust and Reputation Management from the broader IEG literature into clinical-specific models, such as Doctor-AI Adoption and Adaptive Coordination Games. Conclusions: By shifting from static replicator dynamics to Adaptive Learning Strategies (e.g., MARL and Bayesian updating), IEG Health can address critical challenges like algorithm aversion and clinical deskilling. This study provides a structured foundation for future research to transition from theoretical modeling to AI-augmented clinical decision-making.