This lesson from a Yale course on medical software introduces artificial intelligence and machine learning as they apply to healthcare systems. The instructor defines AI, machine learning, and deep learning, then walks through supervised learning's training and application phases before distinguishing classification from regression as the two common ML tasks. The lecture flags practical obstacles developers face, including data scarcity, high dimensionality, and overfitting, and closes by situating these technical choices within the regulatory framework that governs medical software. Aimed at students building a working vocabulary before tackling more advanced material, it stays at the conceptual level rather than working through code or math, functioning as groundwork for later lessons on diagnostic and treatment applications of these tools.