How will AI transform the future of medicine?

Artificial intelligence (AI) has the potential to solve many of humanity’s problems at a pace that far exceeds the limitations of human intelligence. AI excels at detecting errors, minimizing bias, and accelerating decision-making.

Medicine is one industry that can benefit the most from AI. For example, the patient journey today involves trial and error from diagnoses through treatment; similarly, clinical drug development necessitates human experimentation. Fortunately, AI is improving the status quo, from aiding more accurate diagnoses to enabling smaller placebo arm sizes in clinical trials. But computer science experts say we’re only at the beginning of realizing AI’s full potential.

In this Endpoints webinar, hear from AI researcher Charles Fisher, founder and CEO of Unlearn, about the increasingly prominent role of AI medicine. Unlearn was founded to advance AI to eliminate trial and error in medicine, starting with their prognostic digital twins used in clinical trials today and with broader future applications in personalized medicine.


  • Reviewing the current landscape of AI in medicine
  • Discussing some of the most transformative AI applications in the future
  • Providing a potential roadmap connecting the present and future of AI


  • AI/Machine Learning experts and enthusiasts in Pharma and Tech and at Life Sciences VCs

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White Papers

Evaluating Digital Twins for Alzheimer’s Disease using Data from a Completed Phase 2 Clinical Trial

White Papers

Prognostic digital twins overcome the limitations of external control arms in RCTs


European Medicines Agency Qualifies Unlearn’s AI-powered Method for Running Smaller, Faster Clinical Trials

PROCOVA procedure outlines a clear regulatory-qualified framework for implementing AI-generated prognostic digital twins in clinical trials
Both methods reduce control arm sizes, but only digital twins control for bias.
A Phase 2 study on crenezumab in mild-to-moderate AD was used to retrospectively assess the validity of Unlearn's approach for AD clinical trials.