A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations.

A stop smoking mobile app that senses where and when you might be triggered to light up could help people quit - according to University of East Anglia research.

Quit Sense is the world's first Artificial Intelligence (AI) stop smoking app which detects when people are entering a location they used to smoke in. It then provides support to help manage people’s specific smoking triggers in that location.

A team from the Max Planck Institute for the Science of Light (MPL) in Erlangen has created a new, fast and precise method for clinicians to analyse cells in tissue samples from cancer patients without the need for a trained pathologist. They use artificial intelligence to evaluate the data their method produces.

During cancer surgery, fast and accurate information about the operated tissue is required to guide the surgeon's next steps.

A new study by Cedars-Sinai investigators describes how ChatGPT, an artificial intelligence (AI) chatbot, may help improve health outcomes for patients with cirrhosis and liver cancer by providing easy-to-understand information about basic knowledge, lifestyle and treatments for these conditions.

The findings, published in the peer-reviewed journal Clinical and Molecular Hepatology, highlights the AI system’s potential to play a role in clinical practice.

Who can assess and diagnose cardiac function best after reading an echocardiogram: artificial intelligence (AI) or a sonographer?

According to Cedars-Sinai investigators and their research published today in the peer-reviewed journal Nature, AI proved superior in assessing and diagnosing cardiac function when compared with echocardiogram assessments made by sonographers.

A new machine learning model that estimates optimal treatment timing for sepsis could pave the way for support tools that help physicians personalize treatment decisions at the patient bedside, researchers say.

In a paper published today in Nature Machine Intelligence, scientists from The Ohio State University describe the new model, which uses artificial intelligence to take on the complex question of when to administer antibiotics to patients with a suspected case of sepsis.

According to the U.S. Centers for Disease Control, one out of every three adults in the United States has prediabetes, a condition marked by elevated blood sugar levels that could lead to the development of Type 2 diabetes. The good news is that, if detected early, prediabetes can be reversed through lifestyle changes such as improved diet and exercise. The bad news?