This course introduces how AI shapes modern health systems, exploring key applications, adoption trends, and its role in digital transformation. Learners will assess AI’s potential to enhance healthcare capabilities, improve efficiency, and address major challenges. Designed for anyone interested in AI in healthcare, the course offers real-world examples to help participants evaluate AI’s impact, opportunities, and limitations in health systems.

This course explores AI’s role in healthcare decision-making, focusing on data integration, predictive intelligence, and operational impact. Learners will examine AI’s use in clinical and administrative functions, data governance, and workflow integration. Through real-world examples, they will gain insights into AI’s potential, challenges, and strategic implementation in healthcare.

This course covers essential AI concepts in healthcare, starting with key algorithms and their role in decision-making. Learners will explore the craft of AI modeling, from data preparation to evaluation, and examine different model-based algorithms, based on the learning process, such as supervised, unsupervised, and reinforced learning algorithms.

This course begins by exploring agentic AI and AI-driven agents in healthcare, focusing on their role in automation, decision support, and system optimization. Learners will understand how these AI-driven systems enhance healthcare processes before applying their knowledge in a capstone project. Participants will design a comprehensive AI-driven healthcare plan, execute key components, and develop a structured implementation strategy. The course culminates in a detailed report outlining execution insights and future steps for AI integration in healthcare.