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?

As more consumers turn to the newly available ChatGPT for health advice, researchers are eager to see whether the information provided by the artificial intelligence chatbot is reliable and accurate. A new study conducted by researchers at the University of Maryland School of Medicine (UMSOM) indicates that the answers generated provide correct information the vast majority of the time; sometimes, though, the information is inaccurate or even fictitious.

A new artificial intelligence (AI) tool can predict the functions of enzymes based on their amino acid sequences, even when the enzymes are unstudied or poorly understood. The researchers said the AI tool, dubbed CLEAN, outperforms the leading state-of-the-art tools in accuracy, reliability and sensitivity. Better understanding of enzymes and their functions would be a boon for research in genomics, chemistry, industrial materials, medicine, pharmaceuticals and more.

Once adults reach age 65, the threshold age for the onset of Alzheimer's disease, the extent of their genetic risk may outweigh age as a predictor of whether they will develop the fatal brain disorder, a new study suggests.

The study, published recently in the journal Scientific Reports, is the first to construct machine learning models with genetic risk scores, non-genetic information and electronic health record data from nearly half a million individuals to rank risk factors in order of how strong their association is with eventual development of Alzheimer's disease.

Surgical tumor removal remains one of the most common procedures during cancer treatment, with about 45 percent of cancer patients undergoing surgical tumor removal at some point. Thanks to recent progress in imaging and biochemical technologies, surgeons are now better able to tell tumors apart from healthy tissue. Specifically, this is enabled by a technique called "fluorescence-guided surgery" (FGS).

A new article in The Journal of Nuclear Medicine explores the potential for using ChatGPT, an artificial intelligence chatbot, in the field of nuclear medicine and molecular imaging. In the article, Irène Buvat, PhD, and Wolfgang Weber, MD, PhD, report on discussions they held with ChatGPT regarding several nuclear medicine and molecular imaging topics and provide their commentary on the pros and cons of using the chatbot.