Our research focuses on establishing a robust governance framework for safely integrating Artificial Intelligence (AI) into healthcare, emphasizing practitioner empowerment. Despite AI's potential to enhance healthcare processes like disease diagnosis and predictive modeling, challenges remain in translating AI solutions into clinical practice. We address this gap by proposing an AI governance framework focused on the adoption of AI, aiming to bridge the expertise disparity between practitioners and AI systems. In healthcare settings, the end-user is essential for the adoption of new technology. However, this perspective is largely overlooked in the AI governance literature. Shifting this narrative requires a user-centric approach to AI governance, with the end-user centrally positioned in the governance process. Efforts should also be directed at addressing the human factors influencing AI adoption, such as user experience and user empowerment. By incorporating the end-user perspective into the discourse on AI governance, we can create a more comprehensive framework that ensures a responsible and inclusive utilization of AI technologies. For this study, we conduct a multi-case study to design an AI governance framework that encompasses all the intricacies of AI governance practices across different RT centers, with the focus on the key elements to be considered by the AI governance framework. Our research approach will be threefold. In the first stage, we will conduct semi-structured in-depth interviews at 22 different RT centers in a Western European country. During the second stage of our research, we will also analyze various policy documents and internal memos. In the third phase of our research approach, we will step away from solely identifying the core constructs by analyzing how the core constructs influence each other using Qualitative Comparative Analysis (QCA). This study contributes to the evolving understanding of AI governance within IS literature and extends the current knowledge to the healthcare context. By conducting a multi-case study across 22 RT centers, we plan to further identify patterns and core constructs that influence the effective adoption and utilization of AI in these settings. Drawing on insights from interviews with AI users and AI policymakers, we outline key governance conditions that must be met to facilitate the use of AI in healthcare and continue to identify other configuration potential in our future work.