A model developed by researchers at MIT and Boston Children’s Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Read MoreMachine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Read MoreThe PKG Center for Social Impact expands Code.Tulsa experiential learning program.
Read MorePatricia and James Poitras ’63 provide fellowships for graduate students and postdocs who will shape the future of mental health research.
Read MoreThe handheld catheterization device AI-GUIDE, created by Lincoln Laboratory and Massachusetts General Hospital, promises improved health outcomes for injured service members and civilians.
Read MoreThe visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.
Read MoreFounded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.
Read MoreAn AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.
Read MoreResearchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
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