From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems
- URL: http://arxiv.org/abs/2603.30004v1
- Date: Tue, 31 Mar 2026 16:58:15 GMT
- Title: From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems
- Abstract summary: This study uses a Scoping Review with Bibliometric Analysis to map research patterns, identify gaps, and derive policy implications.<n>Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures.<n>Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanced Strategic Diagram (ESD), the study applies the PAGER framework to link Patterns, Advances, Gaps, Research directions, and Evidence-based policy implications. Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures, showing a convergence toward integrated ecosystem architectures where intelligence, trust, and infrastructure reinforce each other. Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration. Emerging themes such as explainable AI, federated learning, and privacy mechanisms highlight areas needing further research. Policy-relevant recommendations focus on coordinated governance, scalable infrastructure, and secure data ecosystems, particularly for developing country contexts. The study bridges bibliometric evidence with actionable policies, supporting informed decision-making in smart hospital development.
Related papers
- An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System [1.6881002551798014]
This study presents an AI implementation case study within Shriners Childrens (SCs), a large multisite pediatric system.<n>We introduce a Python-based data quality assessment tool compatible with SCs infrastructure, extending OHDsi's R/Java-based Data Quality Dashboard.<n>We also compare systematic and case-specific AI implementation strategies for Craniofacial Microsomia (CFM) using the FHIR standard.
arXiv Detail & Related papers (2025-12-01T14:21:16Z) - AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
arXiv Detail & Related papers (2025-08-26T08:38:01Z) - Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
arXiv Detail & Related papers (2025-05-03T13:55:38Z) - Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [132.77459963706437]
This book provides a comprehensive overview, framing intelligent agents within modular, brain-inspired architectures.<n>It explores self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities.<n>It also examines the collective intelligence emerging from agent interactions, cooperation, and societal structures.
arXiv Detail & Related papers (2025-03-31T18:00:29Z) - AI-Enabled Knowledge Sharing for Enhanced Collaboration and Decision-Making in Non-Profit Healthcare Organizations: A Scoping Review Protocol [0.0]
This protocol outlines a scoping review designed to systematically map the existing body of evidence on AI-enabled knowledge sharing in non-profit healthcare organizations.<n>The review aims to investigate how such technologies enhance collaboration and decision-making, particularly in the context of reduced external support following the cessation of USAID operations.
arXiv Detail & Related papers (2025-03-10T17:09:12Z) - SoK: Security and Privacy Risks of Healthcare AI [15.655956766190256]
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care.<n>However, it also exposes sensitive data and system integrity to potential cyberattacks.<n>Current security and privacy (S&P) research on healthcare AI is highly unbalanced in terms of healthcare deployment scenarios and threat models.
arXiv Detail & Related papers (2024-09-11T16:59:58Z) - Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics [1.2374541748245838]
The study aims to analyze the synergy of Artificial Intelligence (AI) with scientometrics, webometrics, and bibliometrics.
Our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication.
arXiv Detail & Related papers (2024-02-22T15:10:02Z) - AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis [0.0]
This study examines 1,903 articles from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines.<n>Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts.<n>The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability.
arXiv Detail & Related papers (2023-12-12T17:47:51Z) - Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning.
The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
arXiv Detail & Related papers (2020-04-29T15:02:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.