Artificial Intelligence (AI) is reshaping how medical data are captured, interpreted, and utilized. This chapter touches upon the history of AI and explores how various AI models are developed, trained, and implemented to process medical data, highlighting how they extract meaningful patterns from structured and unstructured sources such as images, clinical notes, and physiological signals. We discuss foundational model types, including supervised and unsupervised learning systems, neural networks, and deep learning architectures, and how these models are applied to diagnostic imaging, wearable data interpretation, and clinical documentation. In parallel, we examine the ethical challenges that accompany AI integration in medicine, including bias, transparency, accountability, and data privacy. By connecting the technical foundations of AI models with their real-world clinical applications and ethical implications, this chapter aims to provide a comprehensive understanding of how artificial intelligence can support accurate, efficient, and equitable patient care while maintaining trust and responsibility in medical practice.