Unlearn.AI nabs $12M to build “Digital Twins” to speed up and improve clinical trials

Twins have long played a role in the world of medical research, specifically in the area of clinical trials, where they can help measure the effectiveness of a therapy by applying a control to one of a genetically-similar pair. Today, a startup founded by a former principal scientist at Pfizer, which has developed a way of digitising this concept through the use of AI, is announcing some funding to further its efforts. Unlearn.AI, which has built a machine learning platform that builds “digital twin” profiles of patients that become the controls in clinical trials — is announcing that it has raised $12 million in a Series A round.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.

Enter your email address to watch the webinar.

Click the link to watch webinar.
Oops! Something went wrong while submitting the form.
Webinars

Applying Machine Learning to Increase Clinical Trial Efficiency: A Regulatory Journey

Press

Unlearn Closes $50 Million Series B Funding to Advance the Use of Its Machine Learning-Powered TwinRCTs™ in Clinical Trials

Press

Unlearn Signs Multi-Year Collaboration with Merck KGaA, Darmstadt, Germany to Accelerate Immunology Trials using Twintelligent RCTs™

Collaborators will leverage AI-generated Digital Twins to enable smaller, more efficient pivotal clinical trials.
Led by Insight Partners, financing builds on the company’s momentum in working with leading biopharmaceutical companies to improve clinical trial efficiency.
Learn about the evolution of ideas that led to our TwinRCT™ solution for smaller, more efficient clinical trials and recent EMA draft qualification opinion.