A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool that has the potential to help investors identify the next unicorn.
The last thing you want to do when installing a new, free app on your phone is to scroll through pages of information on what kind of access to your personal information it requires. App builders count on this, and their intrusive apps harvest data that they can then sell. That is why University of Groningen computer scientist Fadi Mohsen, together with colleagues from the University of Michigan-Flint (US) and the Palestinian An-Najah National University, has developed an algorithm that ranks similar apps on privacy scores.
It isn’t a matter of one needle puncture. Many children coming through the doors of Children's Hospital Los Angeles are seen for chronic conditions and often require frequent visits. Painful procedures - like a blood draw or catheter placement - can cause anxiety and fear in patients. Now, a study published in JAMA Network Open shows that virtual reality can decrease pain and anxiety in children undergoing intravenous (IV) catheter placement.
Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure - and even such massive efforts are frequently unsuccessful.
Artificial intelligence (AI) will fundamentally change medicine and healthcare: Diagnostic patient data, e.g. from ECG, EEG or X-ray images, can be analyzed with the help of machine learning, so that diseases can be detected at a very early stage based on subtle changes. However, implanting AI within the human body is still a major technical challenge.
A novel artificial intelligence blood testing technology developed by researchers at the Johns Hopkins Kimmel Cancer Center was found to detect over 90% of lung cancers in samples from nearly 800 individuals with and without cancer.
The test approach, called DELFI (DNA evaluation of fragments for early interception), spots unique patterns in the fragmentation of DNA shed from cancer cells circulating in the bloodstream.
A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence.