A commercial artificial intelligence (AI) tool used off-label was effective at excluding pathology and had equal or lower rates of critical misses on chest X-ray than radiologists, according to a study published today in Radiology, a journal of the Radiological Society of North America (RSNA).

Recent developments in AI have sparked a growing interest in computer-assisted diagnosis, partly motivated by the increasing workload faced by radiology departments, the global shortage of radiologists and the potential for burnout in the field.

It may someday be possible to use Large Language Models (LLM) to automatically read clinical notes in medical records and reliably and efficiently extract relevant information to support patient care or research. But recent research from Columbia University Mailman School of Public Health using ChatGPT-4 to read medical notes from Emergency Department admissions to determine whether injured scooter and bicycle riders were wearing a helmet finds that LLM can't yet do this reliably.

The European Union's law on artificial intelligence came into force on 1 August. The new AI Act essentially regulates what artificial intelligence can and cannot do in the EU. A team led by computer science professor Holger Hermanns from Saarland University and law professor Anne Lauber-Rönsberg from Dresden University of Technology has examined how the new legislation impacts the practical work of programmers. The results of their analysis will be published in the autumn.

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.