Ó³»­´«Ã½

Machine Learning for Health (ML4H) is the Ó³»­´«Ã½â€™s clinical AI group. We build and translate machine learning methods that turn real-world clinical data into actionable insights, from earlier detection and risk prediction to deeper understanding of disease biology.

We are a multidisciplinary effort at the Ó³»­´«Ã½, working closely with faculty and collaborators at Massachusetts General Hospital, Brigham and Women’s Hospital, MIT, and beyond.

What we do:

Advance core AI methods for medicine
We develop new machine learning approaches that learn from complex biomedical data — including electronic health records, medical imaging, wearable and sensor data, clinical notes, and genomics.

Deliver models for diagnosis and risk prediction
We build and validate models to identify disease subtypes, predict outcomes, and support clinical trials and decision-making, with a focus on rigorous evaluation and clinical relevance.

Connect clinical signals to biology
We use AI to uncover genetic and molecular drivers of disease and to link phenotypes measured in the clinic to underlying mechanisms.

Build with clinicians and the community
We partner with clinicians, researchers, and engineers to ensure our work is trustworthy, interpretable when needed, and designed to be used in real-world settings.

Learn about our key projects and browse our publications to explore methods, applications, and results.

Collaborate with us

To discuss collaboration opportunities, contact ML4H’s director, Mahnaz Maddah. For general inquiries, email ml4h@broadinstitute.org.