This article emphasizes the critical role of implementation science (IS) in translating artificial intelligence (AI) innovations in medical imaging into routine clinical practice. Despite rapid technological advances, a significant gap exists between AI development and real-world adoption due to organizational, behavioral, and infrastructural barriers. IS offers structured frameworks, models, and strategies—such as hybrid designs and human-centered approaches—to address these challenges. The article advocates for collaborative, stakeholder-driven co-creation, early stakeholder engagement, and multisectoral partnerships to ensure AI tools are useable, sustainable, and impactful. Integrating IS principles is essential to bridge the gap between innovation and clinical impact.