MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
Most antibiotics target metabolically active bacteria, but with artificial intelligence, researchers can efficiently screen compounds that are lethal to dormant microbes.
Joining three teams backed by a total of $75 million, MIT researchers will tackle some of cancer’s toughest challenges.
Although artificial intelligence in health has shown great promise, pressure is mounting for regulators around the world to act, as AI tools demonstrate potentially harmful outcomes.
An interdisciplinary team of researchers thinks health AI could benefit from some of the aviation industry’s long history of hard-won lessons that have created one of the safest activities today.
These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
“FrameDiff” is a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.