Ahead of the Big Data in Precision Health conference, Emma Huang from Johnson & Johnson Innovations discusses collaborations between industry and academia.
Maja Matarić, a robiticist at the University of Southern California, plans to speak about socially assistive robotics at Big Data in Precision Health.
By scouting for a particular immune cell in the blood, scientists can tell which patients with a lung-scarring disease are at higher risk for death.
Understanding the roles of various microbes in the human microbiome is challenging, but statistics can help, Stanford researcher Susan Holmes explains.
The seventh annual Big Data in Precision Health conference will be held May 22 and 23 on the Stanford campus; registration is now open.
The Stanford Institute for Human-Centered Artifical Intelligence will advance AI research, education and more to improve the human condition.
A Stanford researcher has found that patients with heart failure, even if it's relatively mild, are more likely to die within three months after surgery.
Dean Lloyd Minor discusses findings of Stanford Medicine's recently released Health Trends Report.
Artificial intelligence tied to a wearable heart monitor has shown potential to help diagnose irregular heart rhythms, new research shows.
The Stanford Medicine 2018 Health Trends Report found that an explosion of data in medicine is democratizing health care.
A Stanford study highlights a data optimization method for health-risk assessments to lower costs and and improve diagnostic power.
The latest issue of Stanford Medicine magazine explores the potential for digitally driven innovation to transform health education, diagnostics and care.
A Stanford team has developed an algorithm that uses data about tumors to identify new classifications that can provide information about patient outcomes
A Stanford study examines a key aspect of artificial intelligence: If machines provide advice for patient care, who should those machines be learning from?
A new white paper from Stanford Medicine details obstacles and offers solutions for achieving the full potential of electronic health records.
Scientists have developed an algorithm that combines genome sequence data and electronic health information to predict risk for genetic disease.
