20.500.12556/DKUM-98015 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 (IEGs). 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 modelling 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 (IEG)—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. Furthermore, transitioning these models into clinical environments requires the incorporation of structured ethical guidelines, such as ALTAI, to ensure algorithmic accountability. This study provides a structured foundation for future research to transition from theoretical modelling to AI-augmented clinical decision-making. evolutionary games theory intelligent evolutionary games multi-agent reinforcement learning replicator dynamics synthetic near-empty review algorithm aversion evolucijska teorija iger inteligentne evolucijske igre true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/information © 2026 by the authors Članek v reviji 2026-05-08 08:40:49 2026-05-08 08:40:50 2026-05-09 03:09:16 0000-00-00 00:00:00 2026 0 0 19 str. issue 5, [article no.] 444 Vol. 17 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-03-23 2026-04-30 2026-05-05 004.8 277456643 2078-2489 information-17-00444.pdf information-17-00444.pdf 1 E51C914A90F153B65F7E8CB24A1AD3F3 48cd00422d631199a3c93ec1d855426315f21c1e17fd58b0d82d269bafbff270 7ab144cd-4aa9-11f1-a4b2-00155d0001a1 https://dk.um.si/Dokument.php?lang=slv&id=205915 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0