Researchers have developed a machine learning model that can predict chemotherapy-associated nephrotoxicity.
Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays.
Scientists are launching a project to apply machine learning methods to assess the role of climate variables in disease transmission
Researchers have developed an AI algorithm that can detect and identify different types of brain injuries.
Using machine learning, a team of Western computer scientists and biologists have identified an underlying genomic signature for 29 different COVID-19 DNA sequences.
Machine learning will drastically improve brain-computer interfaces and their ability to remain stabilized during use, greatly reducing or potentially eliminating the need to recalibrate these devices.
A portable surveillance device powered by machine learning can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses trends.
By adding infrared capability to the ubiquitous, standard optical microscope, researchers hope to bring cancer diagnosis into the digital era.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
A deep neural network model helps predict healthcare visits by elderly people, with the potential to save millions.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Researchers have discovered that a population of neurons in the brain’s frontal lobe contain stable short-term memory information within dynamically-changing neural activity.
Using machine learning, a prototype microscope teaches itself the best illumination settings for diagnosing malaria.