There has recently been an explosion of interest and activity in personalized medicine. However, the goal of personalized medicine—wherein treatments are targeted to take into account patient heterogeneity—has been a focus of medicine for centuries. Precision medicine, on the other hand, is a much more recent refinement which seeks to develop personalized medicine that is empirically based, scientifically rigorous, and reproducible. In this presentation, we describe several new statistical machine learning developments which advance this quest through discovering individualized treatment rules based on patient-level features. One of these is outcome weighted learning, or O-learning, which directly estimates the decision rules without requiring regression modeling and is thus robust to model misspecification. Several other new developments will also be described, including an extension to continuous treatments such as dose. Illustrative examples will also be given.