Understanding Healthcare Professionals' Adoption and Sustained Use of AI Medical Technologies in Saudi Arabia's Vision 2030 Context

Abstract

Despite Saudi Arabia’s Vision 2030 AI strategies, a significant gap remains between policy measures and the sustained clinical integration of AI in healthcare. This research employs a sequential exploratory mixed methods design to investigate the transition from initial adoption to continuous usage among practitioners. Currently, the qualitative phase is complete, with thematic analysis underway for interviews conducted with physicians, radiologists, and nurses across the Kingdom. Emerging findings reveal that trust in autonomous AI is not a static construct but evolves through distinct phases: from initial distrust to hesitant reliance, and finally to trustful integration. Preliminary data further suggests that workflow complexity and organisational readiness pose more significant barriers to sustained use than the technical performance of the AI itself. Theoretically, this study proposes a framework for "Adaptive Clinical Integration" that identifies key acceptance factors and explores how Agile project management practices can be adapted to manage the unique complexities of AI-Based Medical Devices (ABMDs). By bridging individual acceptance models with agile science, this research provides a context-specific roadmap for Saudi policymakers to ensure the successful, sustained assimilation of high-stakes healthcare AI.