Medical artificial intelligence (AI) and machine learning have progressed rapidly over the past decade, yielding many new products that clinicians must increasingly learn to integrate into clinical practice.1 A common question is, how do AI and machine learning relate to more familiar work from medical statistics?
In the summer of 1956, a group of computer scientists gathered at Dartmouth for a 2-month workshop to discuss what organizer John McCarthy termed artificial intelligence: “the science and engineering of making intelligent machines.”2 From the outset, AI attracted researchers from diverse backgrounds including neuroscience, telecommunications, and formal logic. The field was defined not by any specific methodologic approach but rather by the shared goal of enabling computers to solve new tasks.3 Machine learning is the subfield involving a data-driven approach to AI and received its name from Dartmouth workshop attendee Arthur Samuel, who is credited as coining machine learning while discussing his work at IBM building a computer that plays checkers.4 The core premise of machine learning is that a feasible path toward an intelligent computer is to build a learning computer—a machine that improves from experience and exposure to data.