As artificial intelligence (AI) becomes more common in health care, from managing records to assisting with medication decisions, researchers at the Icahn School of Medicine at Mount Sinai are asking an important question: How well does AI hold up when the workload gets intense at health system scale?

Artificial intelligence (AI) is changing the field and practice of medicine, including legal liability and the perception of who is at fault when a patient experiences harm.

“AI holds promise to improve the quality and safety of health care and to reduce errors and patient harm, but the risk of legal liability is a potential barrier for investment and development of this technology as well as the quality of care,” said Michael Bruno, professor of radiology and of medicine at Penn State College of Medicine.

Can smartphones or smartwatches help detect early signs of neurological or mental illness? Researchers at the University of Geneva (UNIGE) monitored a group of participants wearing connected devices, and used artificial intelligence to analyse data such as heart rate, physical activity, sleep and air pollution. Their findings show that connected devices can accurately predict emotional and cognitive fluctuations, opening new avenues for the early detection of changes in brain health.

The risk of serious or fatal heart disease can be predicted with artificial intelligence (AI) analysis of mammograms, according to research published in the European Heart Journal.

The study shows that AI can be used to assess the build-up of calcium deposits in the arteries of the breast from the standard X-ray mammography scans that are currently used in routine breast cancer screening.

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.