A new digital health study by researchers at Scripps Research shows how data from wearable sensors, such as smartwatches and fitness bands, can track a person’s physiological response to the COVID-19 vaccination.

The study, published in npj Digital Medicine, analyzed sensor data on sleep, activity and heart rate from over 5,600 individuals.

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities in the US and globally. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with the flu, which complicates non-lab diagnoses during certain seasons.

An artificial intelligence (AI) model trained using sequential health information derived from electronic health records (EHR) identified a subset of individuals with a 25-fold risk of developing pancreatic cancer within three to 36 months, according to results presented at the AACR Annual Meeting 2022, held April 8-13.

A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine’s deadliest and most puzzling conditions.

Telemedicine offers emerging opportunities to reduce barriers to obesity care faced by healthcare providers, patients and health plans, according to a paper published online in Obesity, The Obesity Society’s flagship journal.

It has been estimated that by 2030 nearly 80% of adults in the United States will have pre-obesity or obesity.

A major new study in Radiology shows that artificial intelligence (AI) is a promising tool for breast cancer detection in screening mammography programs.

Mammograms acquired through population-based breast cancer screening programs produce a significant workload for radiologists. AI has been proposed as an automated second reader for mammograms that could help reduce this workload.

Researchers at the Yale Cardiovascular Data Science (CarDS) Lab have developed an artificial intelligence (AI)-based model for clinical diagnosis that can use electrocardiogram (ECG) images, regardless of format or layout, to diagnose multiple heart rhythm and conduction disorders.