Artificial intelligence (AI) can solve problems at remarkable speed, but it’s the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists are creating sophisticated formulas that enable AI to interpret massive biological datasets to uncover how diseases start, how the immune system responds, and what treatments might work best.

Researchers at National Jewish Health have shown that subtle increases in lung scarring, detected by an artificial intelligence-based tool on CT scans taken one year apart, are associated with disease progression and survival in patients with fibrotic interstitial lung disease. The findings, recently published in the American Journal of Respiratory and Critical Care Medicine, suggest that computer-based image analysis may provide an earlier, more objective way to identify patients at highest risk for worsening disease.

Researchers at McMaster University and the Massachusetts Institute of Technology (MIT) have made two scientific breakthroughs at once: they not only discovered a brand-new antibiotic that targets inflammatory bowel diseases (IBD), but also successfully used a new type of AI to predict exactly how the drug works. To their knowledge, this a global first for the AI.

A new artificial intelligence tool can scan social media data to discover adverse events associated with consumer health products, according to a study published September 30th in the open-access journal PLOS Digital Health by John Ayers of the University of California, San Diego, U.S., and colleagues.

The constant post-market surveillance of the safety of consumer products is crucial for public health and safety.

How to identify the next dangerous virus before it spreads among people is the central question in a new Comment in The Lancet Infectious Diseases. In it, researchers discuss how artificial intelligence (AI), combined with the One Health approach, can contribute to improved prediction and surveillance.

"Artificial intelligence cannot by itself prevent pandemics, but the technology can be a powerful supplement to the knowledge and methods we already use.

In the world around us, many things exist in the context of time: a bird’s path through the sky is understood as different positions over a period of time, and conversations as a series of words occurring one after another.

Computer scientists and statisticians call these sequences time series. Although statisticians have found ways to understand these patterns and make predictions about the future, modern deep learning AI models struggle to perform just as well, if not worse, than statistical models.

Adiposity - or the accumulation of excess fat in the body - is a known driver of cardiometabolic diseases such as heart disease, stroke, type 2 diabetes, and kidney disease. But getting the full picture of a person’s risk is harder than it may seem. Traditional measures such as body mass index (BMI) are imperfect, conflating fat and muscle mass and not capturing where in the body fat is located.