New technology could transform the ability to accurately interpret HIV test results, particularly in low- and middle-income countries.
Machine learning is a type of artificial intelligence that can be described as a mathematical model where computers are trained to learn to see connections and solve problems using different data sets.
Scientists have used an implanted sensor to record the brain signals associated with handwriting, and used those signals to create text on a computer in real time.
Researchers have shown that a group of small autonomous, self-learning robots can adapt easily to changing circumstances. They connected the simple robots in a line, after which each individual robot taught itself to move forward as quickly as possible.
Artificial intelligence could help to optimise the development of antibody drugs. This leads to active substances with improved properties, also with regard to tolerability in the body.
Researchers combined motion analysis that uses smartphone application and machine learning that uses an anomaly detection method, thereby developing a technique to easily screen for carpal tunnel syndrome.
Researchers have created a machine learning model that helps identify bipolar disorder at earlier stages.
Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.
A machine learning system learns on the job. By continuously adapting to new data inputs, this “liquid network” could aid decision-making in medical diagnosis.
Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
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.
Experts working at the intersection of robotics, machine learning, and physics-based simulation share how computer simulation could accelerate the development of "smart robots" which "might interact with humans"