CTAD Lessons for 2020: More Phase 2 Trials, More Diversity

"Aaron Smith of Unlearn.AI, a startup company that uses machine learning to facilitate trials, suggested another way digital technology could cut enrollment. Machines can use data from the control arms of past trials to develop predictions for how disease will progress in a person depending on his or her baseline characteristics, he said. In effect, computers can generate a digital twin for each enrollee, describing what would likely happen to them without treatment. These digital twins can supplement the physical placebo group of a trial. This would lower the number of participants needed for a given trial, and allow a greater proportion of participants to receive drug rather than placebo. The chance of ending up in the placebo group deters many a potential participant."

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Digital Twins: A Tool for Risk Mitigation in the Era of COVID-19

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CB Insights 2020 Digital Health 150: Unlearn.AI named in List of Most Innovative Digital Health Startups

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Embracing Innovation to Move Forward

At Unlearn, our goal is to use the data available from historical trials, to generate new evidence to inform and advance research.
Unlearn is thrilled to be recognized as a contributing member of the international community of pioneers in health.
In what ways can we mitigate risk and apply innovative solutions to unstable trials in the wake of COVID-19?