This course equips students with the applied knowledge and engineering mindset needed to design and deploy AI solutions in complex healthcare environments. Structured around real-world healthcare workflows, students will explore how artificial intelligence can be integrated to enhance clinical decision-making, improve operational efficiency, and support patient outcomes. Through hands-on projects, learners engage with tools and techniques used in modern healthcare AI systems, from predictive modeling and clinical decision support to robotic process automation and the responsible use of large language models. Students begin by developing a foundational understanding of AI technologies within the healthcare lifecycle, including regulatory and ethical frameworks. They then build and evaluate AI models that augment clinical reasoning, such as risk scoring and diagnostic support. The course also explores the application of generative AI to medical documentation and patient communication and culminates in the implementation of robotic and process automation tools to streamline healthcare workflows. Throughout the course, students will iteratively develop a portfolio-ready healthcare AI solution, working both individually and in teams. By the end, they will have a deep, practical understanding of how to translate AI capabilities into impactful, ethical, and integrated applications within the healthcare system.