Eran Segal

A single system autonomously discovers causal biomedical mechanisms, tests them against millions of participant records, and generates interpretable predictive models that evolve with new evidence.

LeDXA reveals that self-supervised learning can unlock critical health insights from DXA scans, outperforming traditional metrics in predicting disease risk with far fewer resources.

MEDA reveals that integrating domain knowledge and mechanistic constraints is crucial for accurately discovering biological models, outperforming traditional numerical fitting methods.

A generative model of human physiology not only beats existing clinical risk scores at predicting disease, but also accurately simulates the effects of clinical interventions, paving the way for personalized medicine.