Heart attacks are one of the leading causes of death worldwide, and women who suffer a heart attack have a higher mortality rate than men. This has been a matter of concern to cardiologists for decades and has led to controversy in the medical field about the causes and effects of possible gaps in treatment.

Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic

An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations.

If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers.

A Rutgers analysis of dozens of artificial intelligence (AI) software programs used in precision, or personalized, medicine to prevent, diagnose and treat disease found that no program exists that can be used for all treatments.

Precision medicine, a technology still in its infancy, is an approach to treatment that uses information about an individual's medical history and genetic profile and relates it to the information of many others to find patterns that can help prevent, diagnose or treat a disease.

Scientists from the Department of Traumatology and Acute Critical Medicine at the Osaka University Graduate School of Medicine developed an AI algorithm to predict the risk of mortality for patients suffering a major injury. Using the Japan Trauma Data Bank for the years 2013 to 2017, they were able to obtain records for over 70,000 patients who had experienced blunt-force trauma, which allowed the researchers to identify critical factors that could guide treatment strategies more precisely.

Flexible implanted electronics are a step closer toward clinical applications thanks to a recent breakthrough technology developed by a research team from Griffith University and UNSW Sydney.

The work was pioneered by Dr Tuan-Khoa Nguyen, Professor Nam-Trung Nguyen and Dr Hoang-Phuong Phan (currently a senior lecturer at the University of New South Wales) from Griffith University's Queensland Micro and Nanotechnology Centre (QMNC) using in-house silicon carbide technology as a new platform for long-term electronic biotissue interfaces.

An artificial intelligence (AI) algorithm that can detect subtle brain abnormalities which cause epileptic seizures has been developed by a UCL-led team of international researchers.

The Multicentre Epilepsy Lesion Detection project (MELD) used over 1,000 patient MRI scans from 22 global epilepsy centres to develop the algorithm,