Bringing a drug to market requires multiple clinical trials with increasingly larger numbers of patients. Timelines for phase 3 trials can extend as long as four years, making the status quo inefficient and unsustainable. For rare neurodegenerative diseases with few available treatments like Huntington’s Disease (HD) and Amyotrophic Lateral Sclerosis (ALS), longer trial timelines translate to delays in bringing new treatments to patients who need them most. By using machine learning to leverage the wealth of existing patient data from completed clinical trials, our technology reduces the number of patients required in the control arm. Smaller trial sizes shorten typical enrollment timelines by months, enabling patients to access new treatments sooner. Here we describe how our regulatory-suitable TwinRCT solution achieves the goal of reducing control arm sizes by up to 22% for Phase 3 trials in HD and ALS.