Size matters in particle treatments of traumatic injuries
A new analysis offers guidance on the size of nanoparticles that could be most effective at stopping internal bleeding.
A new analysis offers guidance on the size of nanoparticles that could be most effective at stopping internal bleeding.
A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
In stepping down as co-director of the Harvard-MIT Program in Health Sciences and Technology, Brown will work to develop a new center for anesthesiology research.
The sticky patch could be quickly applied to repair gut leaks and tears.
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
New fellows are working on electronic health record algorithms, remote sensing data related to environmental health, and neural networks for the development of antibiotics.
A computational study shows that dozens of mutations help the virus’ spike protein evade antibodies that target SARS-CoV-2.
A pill that releases RNA in the stomach could offer a new way to administer vaccines, or to deliver therapies for gastrointestinal disease.
Scientists demonstrate that AI-risk models, paired with AI-designed screening policies, can offer significant and equitable improvements to cancer screening.
MIT engineers are working on a new kind of device that could streamline the process of blood glucose measurement and insulin injection.
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
The physician, scientist, and professor has made influential contributions to the Harvard-MIT Program in Health Sciences and Technology since it began 50 years ago.
The clinically-trained cell biologist exploits the liver’s unique capacities in search of new medical applications.
SENSE.nano symposium highlights the importance of sensing technologies in medical studies.
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.