A Stanford Medicine magazine article on sex differences in the brain remains popular; this article provides additional information.
Attitudes about gender that male teens encounter during high school can shape their educational achievements and careers, a new study has found.
In his quest to cure his daughter’s ultra-rare disease, Matt Wilsey might also be changing the way drugs are made, Stanford Business magazine reports.
A new data compression technique could pave the way for digital retinas and other brain-controlled machines.
New research suggests why people with epilepsy, even when their seizures are well controlled, report lapses in their ability to think, perceive or remember.
Scientists at Stanford have developed a new PET scan tracer that flags both pancreatic cancer and a lung disease known as idiopathic pulmonary fibrosis.
Jason Melehani, a resident in internal medicine, has had a long and eclectic career path toward developing new therapies to treat tobacco smokers.
Stanford Medicine pulmonologist Mark Nicolls is working with Nobel winner Gregg Semenza to boost the success of lung transplants.
A Stanford biomedical data scientist discusses how computational modeling of big data could help improve personalized chemotherapy selection in the future.
Stanford researchers examined how people react to museum exhibits offering an immersive experience with the single-cell organism Euglena.
A new approach to biobanking that streamlines sample storage and processing is enabling Stanford scientists and doctors to pursue new lines of research.
Through genetic tests and databases of symptoms, doctors in a network of clinical centers help families determine what is affecting their children's health.
A new discovery could provide a way of detecting Parkinson's disease in its earliest stages, before symptoms start. And it could accelerate the development of …
Scientists have used CRISPR-Cas9 screens to reveal more about how the bacteria behind Legionnaire's disease infects humans.
Researchers at Stanford are mining millions of de-identified patient records using machine learning to determine long-term safety of medical devices.
Using neuroimaging and machine learning, researchers were able to predict whether antidepressants would help individual patients.