Using AI to decode the hidden signals of cardiovascular and cardiometabolic disease
Section of Cardiovascular Medicine · Yale School of Medicine
Our work follows a translational arc from computational method development to clinical deployment and discovery. We develop and apply AI to multimodal data already generated in care (including echocardiography, cardiac CT, ECG, and longitudinal health records) to define cardiovascular phenotypes more precisely, detect disease earlier, and match therapies to the patients most likely to benefit.
Create new computational techniques that align, reconstruct, and learn from multimodal clinical data.
Train foundation models and task-specific models for defined cardiovascular questions.
Test performance, transportability, and equity across institutions, populations, and prospective cohorts.
Use validated models to screen for disease, predict risk, and link patient phenotypes to therapies.
Learn from deployment to reveal disease biology and generate the next clinical and computational questions.
We are especially interested in people who want to ask clinically important questions, build rigorous quantitative methods, and test them in real care settings.