Wearable health monitors, ubiquitous sensors, and the ability to collect and store huge amounts of data are creating challenges for researchers hoping to use artificial intelligence to identify diseases. While the gathered data can hold important clinical answers, finding those answers means that the data must be categorized and labeled. Now, researchers at MIT have developed a system that can autonomously identify signs of a disease from data gathered from a relatively small group of people and without any initial training. The research, recently presented at the Machine Learning for Healthcare conference in Ann Arbor, Michigan, focused on learning the audio biomarkers of vocal cord disorders. Using data gathered over a week from an accelerometer attached to the necks of 100 people, the system automatically identified which sound characteristics were important for identifying whether a patient has vocal cord nodules.