Finding a noninvasive way to measure pressure in the brain
MIT researchers team up with Boston Medical Center and Philips to test a noninvasive way to measure intracranial pressure.
Personalized heart models for surgical planning
System can convert MRI scans into 3D-printed, physical models in a few hours.
Turbocharging medical translation
Jeff Karp’s diverse multidisciplinary team brings novel solutions to a wide array of health challenges.
Capturing cell growth in 3-D
Spinout’s microfluidics device better models how cancer and other cells interact in the body.
Real-time data for cancer therapy
Biochemical sensor implanted at initial biopsy could allow doctors to better monitor and adjust cancer treatments.
Study: Firms “underinvest” in long-term cancer research
Tweaks to the R&D pipeline could create new drugs and greater social benefit.
New material opens possibilities for super-long-acting pills
A pH-responsive polymer gel could create swallowable devices, including capsules for ultra-long drug delivery.
How do you hack health care?
With MIT Hacking Medicine, brilliant minds converge at MIT to contribute to design thinking for health care.
Uncovering the mechanism of our oldest anesthetic
MIT researchers reveal brainwave changes in patients receiving nitrous oxide, or “laughing gas.”
Major step for implantable drug-delivery device
MIT spinout signs deal to commercialize microchips that release therapeutics inside the body.
Medical anthropology
Erica Caple James investigates how behavior, culture, and structural inequalities impact health.
Researchers develop a new means of killing harmful bacteria
Engineered particles are capable of producing toxins that are deadly to targeted bacteria.
Making cancer treatment more accessible
MIT collaborates on a smaller, lighter delivery system for proton-beam radiotherapy.
Researchers identify new target for anti-malaria drugs
Manipulating the permeability of a type of vacuole could help defeat malarial parasites.
How a computer can help your doctor better diagnose cancer
Model from MIT's Computer Science and Artificial Intelligence Laboratory aims to automatically distinguish lymphoma subtypes.