Artificial Intelligence (AI) has emerged as a potential tool to help clinicians navigate the data-rich, complex healthcare landscape. This chapter introduces clinicians to the fundamentals of AI, starting with a mental model made up of its parts: compute, data, algorithms, architectures, and models; each component crucial for AI to work. From the decomposition of AI, we will transition to the subtypes of AI, including machine learning (with its branches of supervised, unsupervised, and reinforcement learning), deep learning, and generative AI. We’ll dive into the subtype differences as they relate to their learning approaches and goals. Then, we’ll look at examples of AI application in healthcare include supervised learning for cardiovascular risk prediction, unsupervised learning for diabetes subtype identification, deep learning for intracranial hemorrhage detection, and reinforcement learning for treatment protocol optimization. Finally, we’ll cover the key challenges in using AI in healthcare, which include data security and privacy concerns, the need for model explainability, questions of responsibility for AI-assisted decisions, and ethical considerations regarding data use and potential biases.