With the integration of Artificial Intelligence (AI) and Machine Learning (ML) in medical devices, unprecedented opportunities for automation, precision and efficiency in healthcare sectors have risen. However, these advancements also introduce significant challenges, including data integrity, algorithmic transparency, adversarial robustness, and regulatory compliance. Traditional assurance methods fail to capture the dynamic and evolving nature of AI-driven medical systems. To address these challenges, we discuss a wide range of structured assurance case patterns tailored to AI-enabled medical devices. We explore potential risks that ML-based systems will face and design assurance cases to build trustworthy intelligent systems.