This chapter comprehensively explores AI-enabled medical device testing and clinical validation. It begins by outlining best practices for AI model testing frameworks, followed by an in-depth discussion on AI Model Robustness testing. The chapter then delves into the crucial concepts of AI Model Transparency, explainability, and interpretability, addressing how these can be implemented and how developers can strike a balance between interpretability and performance. A thorough examination of various types of bias and methods for testing bias is presented. The chapter introduces and clearly defines the concepts of human-in-loop and human-in-command, explaining how developers can leverage these approaches to enhance ethical considerations, mitigate bias, and reduce product liability while improving regulatory compliance. The discussion then transitions to clinical validation, offering strategies for successful clinical studies and improving AI model generalization. A review of real-world evidence testing is provided, and the chapter concludes with an analysis of two crucial regulatory requirements: the US FDA and EU MDR for Clinical evaluation and related documentation. This comprehensive overview equips readers with the essential knowledge needed to navigate the complex landscape of AI-enabled medical device development and validation.