How to create “humble” AI
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
Researchers 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.
Assistant Professor Matthew Jones is working to decode molecular processes on the genetic, epigenetic, and microenvironment levels to anticipate how and when tumors evolve to resist treatment.
From early motion-sensing platforms to environmental monitoring, the professor and head of the Program in Media Arts and Sciences has turned decades of cross-disciplinary research into real-world impact.
By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
Co-founded by an MIT alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.
MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.