A Stanford University research team used machine learning to quickly and accurately diagnose autism in children through short home videos, according to a research article published in PLOS Medicine.

Using a deep-learning model, a University of Massachusetts Lowell research team was able to significantly improve the extraction of adverse drug events (ADEs) from electronic health records (EHRs).

After using deep learning on patient scans to track cancer evolution, one research team is hopeful the “promising results” can help improve treatment response and survival predictions for cancer patients.

A physician whose research produced promising results for using AI to improve the detection of tuberculosis (TB) was awarded the Alexander R. Margulis Award for Scientific Excellence during the annual RSNA conference in Chicago.

Australian researchers are encouraging physicians to consider how online support groups may impact a cancer patient’s decision-making following a study that used a machine-learning framework to analyze patient interactions within online groups.

The National Institute on Aging has awarded a total of $5 million for two projects that will use AI and big data to better understand Alzheimer’s disease and other memory loss diseases.

The Blavatnik Family Foundation has pledged $200 million to Harvard Medical School, the university announced last week.

Researchers with the Georgia Institute of Technology have developed an open source, machine-learning tool that accurately predicted how cancer patients would respond to specific chemotherapy drugs.

Researchers at the University of California, Los Angeles created a cheap, portable device that uses artificial intelligence (AI) to recognize and classify common airborne allergens.

A Japanese research group has developed a system that uses AI to automatically detect abnormalities in fetal hearts from ultrasound images.

A research team has been awarded a grant to develop a mobile application aimed at raising awareness about diabetes for people in India who are most at risk for developing the disease.

A team of researchers have created a simpler version of electronic wearable devices that can be used for physiological monitoring and alert a user of any health risks in real time.