Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
Artificial intelligence has unimaginable potential to revolutionize medicine. We cover the latest technology breakthroughs of machine learning and deep learning algorithms that process mindboggling amounts of data, spot even the smallest detail in medical images and help medical professionals in designing treatment plans.
Using theoretical calculations, scientists showed that it would not be possible to control a superintelligent AI.
Physicians who follow AI advice may be considered less liable for medical malpractice than is commonly thought, according to a new study of potential jury candidates in the U.S.
Scientists have developed a machine learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.
NIH BRAIN Initiative scientists used machine learning to redesign a bacterial ‘Venus flytrap’ protein that can monitor brain serotonin levels in real time.
A new eye test may predict wet age-related macular degeneration, a leading cause of severe sight loss, three years before symptoms develop.
Is it possible to read a person's mind by analyzing the electric signals from the brain? The answer may be much more complex than most people think.
An AI platform derives an optimal combination of available therapies against SARS-CoV-2 - the optimal drug therapy was a combination of the drugs remdesivir, ritonavir, and lopinavir at specific doses.
If Alzheimer's dementia is identified early, the decline in neural functioning can be stabilized or even curtailed in some cases.
Researchers have developed an AI tool that can measure the volume of cerebral ventricles on MRIs in children within about 25 minutes.
Researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output.
Machine learning can be used to fill a significant gap in Canadian public health data related to ethnicity and Aboriginal status, according to research by a University of Alberta research epidemiologist.
With the advent of pharmacogenomics, machine learning research is well underway to predict patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses.