TwinRCTs™

Make trials more patient-centric

Clinical trials with smaller control groups are more attractive to patients.

Shorten enrollment timelines

TwinRCTs™ reach enrollment targets faster because they require fewer patients.

Increase power and confidence

TwinRCTs™ provide reliable evidence suitable for pivotal clinical trials.
TwinRCTs™ incorporate prognostic information from Digital Twins into randomized controlled trials to enable smaller control groups while maintaining power and generating evidence suitable for supporting regulatory decisions.

TwinRCTs™

Randomized controlled trials with Digital Twins

Prospectively incorporating Digital Twins into clinical trials enables smaller, more efficient trials that maintain power.

Digital Twins are simulated control outcomes generated by AI models trained on historical data.
We prepare a highly curated dataset of historical trials and registry data that is representative of clinical trials in a target indication.

Creating a training dataset

Once the dataset is prepared, we use patent pending machine learning methods to train a Digital Twin generator and then evaluate its performance on a test dataset to ensure it is ready to begin generating Digital Twins.

DiGenesis™: training a Digital Twin generator

What is a Digital Twin?

A comprehensive, longitudinal, and computationally generated clinical record that describes what would have happened if a specific patient had received a placebo.
While a patient’s journey in a clinical trial takes months, or even years, a Digital Twin’s record is created after the first visit. Our Digital Twin generator uses baseline data to create comprehensive predictions of disease progression for each patient.

Creating a Digital Twin

Make your trials faster with TwinRCTs™

We partner with pharmaceutical companies to design and execute efficient clinical trials.

Email us at partnerships@unlearn.ai

Contact us

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