A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.

An artificial intelligence (AI)-based technology rapidly diagnoses rare disorders in critically ill children with high accuracy, according to a report by scientists from University of Utah Health and Fabric Genomics, collaborators on a study led by Rady Children’s Hospital in San Diego.

Predictive, preventive, personalized and participatory medicine, known as P4, is the healthcare of the future. To both accelerate its adoption and maximize its potential, clinical data on large numbers of individuals must be efficiently shared between all stakeholders. However, data is hard to gather. It’s siloed in individual hospitals, medical practices, and clinics around the world.

The new study found that by tweaking the electrical properties of individual cells in simulations of brain networks, the networks learned faster than simulations with identical cells.

They also found that the networks needed fewer of the tweaked cells to get the same results, and that the method is less energy intensive than models with identical cells.

Researchers at Washington University School of Medicine in St. Louis have developed an approach to estimating when a person who is likely to develop Alzheimer’s disease, but has no cognitive symptoms, will start showing signs of Alzheimer's dementia.

The algorithm, available online in the journal Neurology, uses data from a kind of brain scan known as amyloid positron emission tomography (PET) to gauge brain levels of the key Alzheimer's protein amyloid beta.

A new study has shown that issues surrounding trust are still at the heart of people's reluctance to download and use the NHS App, particularly among Black, Asian and minority ethnic (BAME) communities.

Researchers from the University of Nottingham's Horizon Digital Research Institute and the Trustworthy Autonomous Systems (TAS) Hub undertook a survey of

Telemedicine appointments combined with in-person visits significantly reduced the risk of further illness for children with medically complex cases, according to results of a new study by researchers with The University of Texas Health Science Center at Houston (UTHealth).

Children with medically complex cases require intense care supervision for conditions like genetic diseases, feeding difficulties, and developmental delays.