A study showed that an AI algorithm provides results comparable with lung function tests, which measure how forcefully a person can exhale.
Researchers have developed a new approach to early diagnosis of lung cancer: a urine test that can detect the presence of proteins linked to the disease.
An AI algorithm is capable of diagnosing 134 skin disorders and supporting specialists by augmenting the accuracy of diagnoses and predicting treatment options.
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.
The UNC School of Medicine lab of Jason Franz, PhD, created virtual reality experiments to show how a potentially portable and inexpensive test could reduce falls and related injuries in people with multiple sclerosis.
A coronavirus app coupled with machine intelligence will soon enable an individual to get an at-home risk assessment based on how they feel and where they've been in about a minute.
A wearable sensor could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs and could prevent hospitalization.
Researchers have developed a ‘heater’ — about the size of a pill tablet — that regulates the temperature of biological samples through the different stages of diagnostic testing.
Researchers describe a way to increase the sensitivity of biological detectors to the point where they can be used in mobile and wearable devices.
Researchers at the University of Connecticut have developed a lensless microscope that allows an observer to enjoy an enormous field of view.
A deep learning model can identify sleep stages as accurately as an experienced physician.