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Artificial intelligence is not the future of healthcare. It is already a reality in medicine, and its importance in improving healthcare delivery is expected to grow substantially in future decades. As of June 2024, 950 AI-powered medical devices have been cleared for clinical use by the U.S. Food and Drug Administration. The recent boom in generative AI has further fueled enthusiasm for medical AI, presenting a rare opportunity to address challenges like aging populations, declining productivity, rising costs, and disparities in access to care. This course explores the intersection of AI and the business of health, introducing key AI concepts, including its mathematical and computational foundations, economic forces, and its impact on healthcare. Students will learn how to develop predictive and generative AI applications that transform structured and unstructured healthcare data into tools that enhance patient outcomes, streamline operations, and create value in the health sector. The course will cover AI strategies in various healthcare contexts, including patient care, medical research, healthcare operations, and public health supply chains. A key focus will be on the science of scaling medical AI, integrating AI into clinical workflows to improve productivity, access, and equity in healthcare. The course will also address the importance of sustainable reimbursement models for medical AI, exploring different paths AI creators can take towards reimbursement and the roles of various stakeholders in this process. Additionally, students will develop AI leadership skills that synthesize human and non-human intelligence, with an understanding of AI’s inner workings and limitations, including how AI can reinforce or mitigate human biases in healthcare. By the end of the course, students will be equipped to lead the integration of AI into healthcare systems, ensuring that AI’s benefits are broadly and appropriately realized, supported by sustainable financial models.