From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems

AI adoption in hospitals is advancing, but governance lags behind: The research reveals that while AI, blockchain, and cloud computing technologies are progressing in healthcare settings, critical gaps remain in governance frameworks and real-world implementation—meaning hospitals may be deploying intelligent systems without adequate policy structures to oversee them responsibly.

Privacy and security require coordinated approaches across multiple layers: The study identifies three interconnected technological clusters (AI systems, privacy-preserving digital ecosystems, and cloud infrastructure) that must work together. This suggests that protecting patient data requires coordinated governance across all these layers rather than isolated security measures.

Explainable AI and federated learning are emerging priorities: The research highlights that emerging responsible AI approaches—particularly explainable AI (making AI decisions understandable to humans) and federated learning (training AI without centralizing sensitive data)—are gaining research attention, indicating the field is recognizing the need for more transparent and privacy-conscious AI systems.

Developing countries face unique implementation challenges: The study specifically notes that policy recommendations must account for developing country contexts, suggesting that smart hospital ecosystems cannot be implemented with one-size-fits-all approaches and require tailored governance considering local infrastructure and resources.

Interoperability remains a critical unresolved challenge: Despite technological advances, the research identifies persistent gaps in getting different hospital systems to work together seamlessly, which has implications for both patient safety and the ability to implement consistent responsible AI practices across healthcare networks.