A third of cancer patients face chronic pain - a debilitating condition that can dramatically reduce a person's quality of life, even if their cancer goes into remission.

Although doctors have some tools for addressing chronic pain, figuring out who is most at risk for developing it is no easy feat. But a new study, conducted by researchers at the University of Florida and other institutions, uses artificial intelligence (AI) to predict which breast cancer patients are most at risk for developing chronic pain.

In a hopeful sign for demand for more safe, effective antibiotics for humans, researchers at The University of Texas at Austin have leveraged artificial intelligence (AI) to develop a new drug that already is showing promise in animal trials.

Publishing their results in Nature Biomedical Engineering, the scientists describe using a large language model - an AI tool like the one that powers ChatGPT - to engineer a version of a bacteria-killing drug that was previously toxic in humans, so that it would be safe to use.

A video-processing technique developed at the University of Florida that uses artificial intelligence will help neurologists better track the progression of Parkinson's disease in patients, ultimately enhancing their care and quality of life.

The system, developed by Diego Guarin, Ph.D., an assistant professor of applied physiology and kinesiology in the UF College of Health and Human Performance, applies machine learning to analyze video recordings of patients performing the finger-tapping test, a standard test for Parkinson's disease that involves quickly tapping the thumb and index finger 10 times.

Researchers at the National Institutes of Health (NIH) found that an artificial intelligence (AI) model solved medical quiz questions - designed to test health professionals’ ability to diagnose patients based on clinical images and a brief text summary - with high accuracy. However, physician-graders found the AI model made mistakes when describing images and explaining how its decision-making led to the correct answer.

Large language models may pass medical exams with flying colors but using them for diagnoses would currently be grossly negligent. Medical chatbots make hasty diagnoses, do not adhere to guidelines, and would put patients' lives at risk. This is the conclusion reached by a team from the Technical University of Munich (TUM). For the first time, the team has systematically investigated whether this form of artificial intelligence (AI) would be suitable for everyday clinical practice.

A packed auditorium with over 1000 students. This is not a rare sight in introductory informatics lectures. To meet the needs of each individual student under these conditions, Stephan Krusche, professor of Software Engineering, and his team have been building the Artemis learning platform since 2016. It resembles well-known learning platforms, but offers more possibilities.

Researchers at Weill Cornell Medicine have used machine learning to define three subtypes of Parkinson's disease based on the pace at which the disease progresses. In addition to having the potential to become an important diagnostic and prognostic tool, these subtypes are marked by distinct driver genes. If validated, these markers could also suggest ways the subtypes can be targeted with new and existing drugs.