Five MIT faculty members take on Cancer Grand Challenges
Joining three teams backed by a total of $75 million, MIT researchers will tackle some of cancer’s toughest challenges.
Abdul Latif Jameel Clinic for Machine Learning in Health
What to do about AI in health?
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.
Stratospheric safety standards: How aviation could steer regulation of AI in health
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.
2023-24 Takeda Fellows: Advancing research at the intersection of AI and health
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
Twelve with MIT ties elected to the National Academy of Medicine for 2023
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
A more effective experimental design for engineering a cell into a new state
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
How an archeological approach can help leverage biased data in AI to improve medicine
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.
How to help high schoolers prepare for the rise of artificial intelligence
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
MIT researchers combine deep learning and physics to fix motion-corrupted MRI scans
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
Generative AI imagines new protein structures
“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.
MIT scientists build a system that can generate AI models for biology research
BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.
Using AI, scientists find a drug that could combat drug-resistant infections
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.
Is medicine ready for AI? Doctors, computer scientists, and policymakers are cautiously optimistic
With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.
Five MIT faculty elected to the National Academy of Sciences for 2023
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.
Speeding up drug discovery with diffusion generative models
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.