AI-Assisted Healthcare Systems Engineering is an interdisciplinary course that prepares students to integrate artificial intelligence (AI) into the systems engineering lifecycle of healthcare delivery. Built upon the Kossiakoff systems engineering model, the course guides students through foundational concepts in AI, systems thinking, and healthcare operations. Students will examine how AI technologies—such as machine learning, natural language processing, and simulation—can be applied to improve clinical decision-making, administrative processes, and overall system performance in complex healthcare environments.Throughout the course, students engage with practical tools and methods to design, model, and evaluate AI-enabled healthcare systems across all lifecycle phases—from stakeholder needs analysis to system retirement. Key areas of focus include requirements engineering, system architecture, modeling and simulation, ethical and regulatory considerations, test and evaluation, and lifecycle monitoring. Real-world case studies and simulations help students explore how AI can address challenges in areas like clinical decision support, personalized medicine, supply chain logistics, and process optimization.The course culminates in a capstone project where the students individually design a comprehensive, AI-enabled healthcare system approach that demonstrates lifecycle integration. By the end, participants will be equipped with a multidisciplinary skill set—combining technical systems knowledge, AI tools, ethical analysis, and cross-functional communication—to lead innovation and transformation in healthcare settings.