Unlearn’s First Ever Webinar Series, Powering Clinical Research with AI, is available On-Demand

This year, Unlearn launched our first-ever, 3 part webinar series, Powering Clinical Research with AI. These webinars served as a unique opportunity to educate subject matter experts and industry leaders about the transformative potential of AI in clinical research. Now, more than ever, it is critical to embrace innovation to accelerate the clinical trial process, so that new treatments are made available to patients sooner who urgently need them. Our series provides a comprehensive look at how Digital Twins, or AI-generated control outcomes for trial participants, are created, and how this technology enables smaller, more efficient trials. Experts also share regulatory perspectives, forecasts, and strategies for AI-based drug development tools like Digital Twins. All 3 webinars are available to watch, on-demand, below. We encourage you to reach out to partnerships@unlearn.ai to discuss how Digital Twins can accelerate your clinical research, or if you have any questions about our novel approach. 

Part 1: AI, Digital Twins, and the Future of Clinical Research

In our first webinar, our Founder & CEO, Charles Fisher, explains how Digital Twins are created, and how they can be incorporated into trials to increase statistical power, accelerate trial timelines, and enable patient level insights. 

Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins

Our second webinar provides an overview of specific use cases for Digital Twins, where two of our founders walk through how novel trial designs with Digital Twins enable smaller trials that maintain their power.

Part 3:  Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape

In the third installment, Charles Fisher of Unlearn, Diane Shoda of Greyscaling LLC, and Wade Ackerman of Covington & Burling LLP lead a panel discussion on the regulatory landscape and share expert perspectives on the future of AI-based drug development tools like Digital Twins.

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


Bayesian prognostic covariate adjustment


Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)

We showed that digital twins could reduce the number of control subjects required in the analysis to achieve equivalent results to an analysis of the actual subjects.
Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.
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.