Joshua Angrist, Gang Chen, Catherine Drennan, Dina Katabi, Gregory Stephanopoulos, and seven additional alumni are recognized by their peers for their outstanding contributions to research.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.
Seven researchers, along with 14 additional MIT alumni, are honored for significant contributions to engineering research, practice, and education.
Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.
Health benefits of using wind energy instead of fossil fuels could quadruple if the most polluting power plants are selected for dialing down, new study finds.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
New system can teach a group of cooperative or competitive AI agents to find an optimal long-term solution.
With donuts and cider in hand, students, faculty, and staff gathered on Hockfield Court to speak with President-elect Sally Kornbluth and celebrate her appointment.
An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
The brilliant scientist was known for both the depth of his intellect and his kindness.
Study shows AI can identify self-reported race from medical images that contain no indications of race detectable by human experts.