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TwinRCTs improve the ability to observe treatment effects in early stage studies by increasing power without adding more participants.
TwinRCTs reach full enrollment sooner because they require fewer participants to achieve the same power as traditional trial designs.
TwinRCTs give participants a greater chance of receiving the experimental treatment because they use smaller control groups.
A trial participant’s digital twin is a forecast of their clinical outcomes on placebo. We create them using artificial intelligence trained on patient-level data from previous clinical trials and disease registries.
We calculate prognostic scores for each participant in a randomized clinical trial using their digital twins. Adjusting for these scores in the analysis increases power while adhering to guidance from the FDA and EMA.
Explore both traditional and AI-powered clinical trial designs that use digital twins. Make faster decisions to achieve your clinical program goals.