Pilitsis et al. (2025) reveal that a decision tree‐based machine learning model accurately predicts spinal cord stimulation surgery outcomes by analyzing EEG features. Similar to a smart diagnostic tool, the study identifies key neural markers that distinguish responders, paving the way for improved patient selection in chronic pain treatment.