The Impact of AI Integration in Healthcare: Exploring Regulatory, Cultural, and Strategic Barriers

Abstract

This thesis investigates the barriers and enablers to predictive AI adoption in healthcare through a thematic synthesis of 13 academic articles and real-world case studies published over the last five years. Barriers were categorized into three domains: regulatory, cultural, and strategic. These included challenges such as fragmented regulation, clinician skepticism, data quality limitations, and poor alignment with clinical workflows. Cross-cutting patterns, stakeholder tensions, and recurring meta-themes revealed that these barriers are deeply interconnected. Drawing from over 200 individual findings, an actionable visual framework was developed to guide responsible and sustainable predictive AI integration. The proposed model, consisting of an internal “Pyramid” of enablers and an external “Circular Loop” of ecosystem conditions, provides a practical structure for aligning governance, engagement, and workflow with ongoing commitments to equity, collaboration, safety, and transparency.