More than 4 in 10 adults in the UK are happy to use ChatGPT for their mental health support, new research suggests.

The study, led by Bournemouth University surveyed nearly 31,000 adults in 35 countries about their use of Artificial Intelligence (AI) large language models such as ChatGPT. The research also discovered that:

A research team funded by the National Institutes of Health (NIH) has developed a versatile machine learning model that could one day greatly expand what medical scans can tell us about disease. Scientists used their tool, named Merlin, to assess 3D abdominal computed tomography (CT) scans, accomplishing tasks as simple as identifying anatomical features to as complex as predicting disease onset years in advance.

Imagine being able to assess how healthy the front of our eyes are not only in hospitals, but also in remote eye-screening camps, elderly-care facilities, pharmacies, or even train stations. That is the future a research team led by Professor Toru Nakazawa at the Graduate School of Medicine, Tohoku University is working towards with a newly developed portable AI-powered scanning slit-light device.

Telemedicine visits are five times less costly than in-person appointments for the most common conditions able to be treated by both forms of visits, new research from the Perelman School of Medicine at the University of Pennsylvania shows. On average, telemedicine patient visits were billed $400 less, and they also resulted in fewer follow-up visits after the initial appointment.

New research warns that popular deep learning systems trained for cancer pathology may be relying on hidden shortcuts rather than genuine biological signals.

Artificial intelligence (AI) tools are increasingly being developed to predict cancer biology directly from microscope images, promising faster diagnoses, and cheaper testing.

ChatGPT Health, a widely used consumer artificial intelligence (AI) tool that provides health guidance directly to the public - including advice about how urgently to seek medical care - may fail to direct users appropriately to emergency care in a significant number of serious cases, according to researchers at the Icahn School of Medicine at Mount Sinai.

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in the scientific journal Advanced Intelligent Systems.