We invent, train, and deploy generative models capable of generating accurate, comprehensive forecasts of a patient’s health over time under relevant scenarios—which we call their digital twin.
Data is the bedrock of machine learning. We’re building a strong foundation of highly curated datasets consisting of individual, longitudinal, research-consented patient data across our target indications.
Our research is at the cutting edge of generative AI for multivariate time-series. We build and deploy new types of machine learning models for forecasting potential health outcomes for individual patients.
We develop novel methods for using the predicted health outcomes from patients’ digital twins to improve clinical research. The outcome: efficient processes that determine the best treatments for each patient.
A patient’s digital twin forecasts their potential future health outcomes
The human body is too complex to simulate from the atom-up. We’re taking a top-down approach, leveraging artificial intelligence trained on highly curated historical datasets to forecast health outcomes so that we can accelerate medical innovation today.
We envision a future where patients’ digital twins help doctors create more accurate diagnoses and more personalized treatment plans.
Patients’ digital twins will predict relative treatment effects, revealing how different treatments compare to each other with less patient experimentation.
An AI-powered future will enable a more efficient, ethical, and reliable healthcare system.