Key points are not available for this paper at this time.
The integration of Artificial Intelligence (AI) into healthcare systems has become a vital element of digital transformation, enabling smart hospitals to enhance clinical efficiency, diagnostic accuracy, and overall patient care. However, AI adoption in healthcare remains complex due to interrelated technological, organisational, human, and governance-related factors. Therefore, this study aims to identify these factors/enablers and examine their hierarchical interactions to support systematic AI adoption in hospital. Using a Systematic Literature Review (SLR) and expert validation, ten critical enablers were identified. Interpretive Structural Modeling (ISM) was then employed to model the structural relationships among these enablers, followed by MICMAC analysis to classify them based on driving and dependence power. The findings reveal that digital infrastructure, hospital capacity for AI, clear policies and regulations, and collaboration serve as strong driving enablers forming the foundation for AI readiness for smart hospitals. Meanwhile, AI experts, training datasets, AI literacy in medical education, and data quality act as linkage enablers with both high driving and dependence power. The ethical use of AI and hospital readiness to adopt AI emerge as dependent enablers that reflect the outcome of effective governance and infrastructure. This study offers actionable insights for policymakers, hospital administrators, and researchers to design strategic interventions that foster ethical, sustainable, and data-driven healthcare transformation. This study is among the first to systematically model the hierarchical and causal structure of enablers governing the integrated adoption of AI in smart hospitals using an ISM–MICMAC framework.
Shahbaz Khan (Fri,) studied this question.