How are we changing clinical trials?

How are we changing clinical trials?

How are we changing clinical trials?

White Papers

Summary of the EMA March 2022 Draft Qualification Opinion for PROCOVA™

Learn more about the highlights, implications, and FAQs concerning the European Medicines Agency’s draft qualification opinion for our 3-step PROCOVA™ procedure.
Blog

Success is never a straight line—reflecting on our EMA draft qualification opinion

White Papers

Applications of Digital Twins in Clinical Trials for Alzheimer’s Disease

Podcasts

Finding Genius Podcast: AI in Healthcare: Artificial Intelligence is the Way to Advance into the Next Era of Medical Technology

AI in healthcare may sound like science fiction, but thanks to Charles Fisher, Ph.D., and his company, Unlearn.ai, it is now quickly becoming a reality.
How Digital Twins make it possible to design and run more efficient clinical trials with well-defined statistical properties.
On March 22, 2022, we reached an historic milestone with the European Medicines Agency’s release of a draft qualification opinion on our 3-step PROCOVA™ procedure.

Using AI-based Prognostic Models to Design Efficient, Unbiased Clinical Trials

The Bio Report Podcast: Using AI Created Digital Twins to Accelerate Clinical Trials

White Papers

PROCOVA™ Handbook for the Target Trial Statistician

This handbook provides step-by-step guidance for the practical application of our PROCOVA™ procedure for Phase 2 or 3 TwinRCTs™.
Publications

Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins

Podcasts

Bloomberg Businessweek Podcast: Using AI Digital Twins for Drug Testing

Press

Digital Twin Advances Poised to Expand Clinical Trial Reach

Digital twins enable faster recruitment with higher powered, individualized trials [capable of generating] additional data from the same or fewer number of patients.
Dr. Charles Fisher, CEO of Unlearn AI, discusses creating digital clones by using artificial intelligence for use in clinical drug trials.
Here, we have demonstrated that a particular type of generative model (i.e., CRBMs) can be used to accurately model disease progression for patients with MCI or AD.

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