Artificial Intelligence–Enabled Smart Hospitals as Integrated Socio‑Technical Systems: A Scoping Review

Hospitals are undergoing digital transformation through artificial intelligence (AI) and related Healthcare 4.0 technologies, giving rise to the concept of AI‑enabled or smart hospitals as integrated socio‑technical systems. While numerous reviews catalog AI applications in healthcare, limited synthesis exists on how the literature conceptualizes AI as a defining, integrating feature of hospital‑level systems. We conducted a scoping review following established methodology, searching PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Semantic Scholar for peer‑reviewed literature published 2015–2025. We included studies that addressed AI‑enabled or smart hospitals as systems spanning clinical, operational, infrastructural, or governance domains. Twenty‑three publications met the inclusion criteria, comprising conceptual/architectural papers, narrative reviews, empirical studies, and one bibliometric analysis. Most studies described AI use cases across multiple domains but treated them as discrete applications rather than integrated systems. A smaller subset presented explicit system‑level architectures (e.g., layered IoT‑based designs, digital twin‑based cyber‑physical systems) or institutional case studies positioning AI as core infrastructure. Empirical studies consistently identified sociotechnical barriers—data fragmentation, interoperability limitations, governance gaps, workforce capacity, and workflow misalignment—as primary implementation challenges. The conceptual foundation for AI‑enabled hospitals as integrated systems exists, but rigorous evaluations of fully implemented systems remain scarce. Advancing from AI applications to truly AI‑enabled hospitals requires progress in reference architectures, governance frameworks, implementation science, and system‑level evaluation with attention to equity and societal impact.