Mental health disorders represent a major global challenge, driving demand for scalable, evidence based strategies in early diagnosis, prevention, and population surveillance. Business intelligence (BI) research in this domain remains conceptually and methodologically fragmented, impeding interdisciplinary synthesis. This study investigates: the main themes in BI applications for mental health and healthcare over the past 30 years; patterns within and across abstracts of BI relevant articles, including integration potential; and themes with the strongest citation. By doing so, we contribute to health marketing scholarship by synthesizing fragmented BI applications into a Service-Dominant Logic SDL informed, market-shaping intelligence perspective. This reframing is an interpretive synthesis: it is informed by the STM findings and developed through their integration with established health marketing theory, rather than being a direct empirical output of the topic model. Using structural topic modeling (STM) on 2,227 peer reviewed articles from the Web of Science Core Collection (1990-2025), after PRISMA based screening of 20,634 records, the analysis reveals 20 latent topics that trace the evolution of BI, data mining, and dashboard analytics in mental health. We contribute to the literature by offering a novel perspective on Business Intelligence in health marketing, showing how fragmented analytical streams can be synthesized into a Service Dominant Logic informed Mental Health Intelligence Platform under conditions of interdisciplinary fragmentation and temporal evolution Results reveal a shift from individual psychological foci toward systemic, computational approaches underpinning Mental Health Intelligence Platforms (MHIPs). This study outlines a research agenda for empirically evaluating Mental Health Intelligence Platform MHIP designs in future health marketing research.
Omar Alsodi, Mohammad Alhur, Nicholas Grigoriou et al.· Health Marketing Quarterly· 0 citations
The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids.
Reham Alsbua, M. Al-Soeidat, Ahmad A. Salah et al.· Energies· 0 citations
