| Title: | Bridging the knowledge void : a synthetic near-empty review of intelligent evolutionary games’ employment in healthcare |
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| Authors: | ID Kokol, Peter (Author)
ID Blažun Vošner, Helena (Author)
ID Završnik, Jernej (Author)
ID Žlahtič, Bojan (Author) |
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| Files: |  information-17-00444.pdf (1,92 MB)
MD5: E51C914A90F153B65F7E8CB24A1AD3F3
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.02 - Review Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | evolutionary games theory, intelligent evolutionary games, multi-agent reinforcement learning, replicator dynamics, synthetic near-empty review, algorithm aversion |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 23.03.2026 |
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| Article acceptance date: | 30.04.2026 |
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| Publication date: | 05.05.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 17, issue 5, [article no.] 444 |
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| PID: | 20.500.12556/DKUM-98015  |
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| UDC: | 004.8 |
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| ISSN on article: | 2078-2489 |
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| COBISS.SI-ID: | 277456643 |
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| Copyright: | © 2026 by the authors |
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| Publication date in DKUM: | 08.05.2026 |
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| Views: | 120 |
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| Downloads: | 3 |
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| Metadata: |    |
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| Categories: | Misc.
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