In a little over 18 months, the novel coronavirus (Sars-CoV-2) has infected over 18 million people and caused more than 690,000 deaths. The current standard for diagnosis through reverse transcription polymerase chain reaction is limited owing to its low sensitivity, high rate of false positives, and long testing times. This makes it difficult to identify infected patients quickly and provide them with treatment.

Researchers are developing a program that could be added to the COVID alert app used on smartphones to better target vaccination campaigns.

The COVID alert app is based on the Google-Apple exposure notification API (GAEN API), a functionality that the tech companies rolled out in April 2020. The Canadian government built an app around the GAEN API, which became the COVID alert app, and managed the system for uploading positive cases.

Using telemedicine, COVID-19 patients can be cared for safely at home - from initial home isolation to recovery or, in case problems arise, admission to hospital. A team from the Technical University of Munich (TUM) has now successfully demonstrated this in a study involving 150 patients with risk factors for a severe progression of the disease.

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.