Cortechs.ai, a San Diego, CA-based provider of AI-powered quantitative imaging solutions, has announced the expansion of its European CE Marking under the European Union Medical Device Regulation (EU MDR) for its latest imaging products.
The DeepHealth Breast Ultrasound system automates the detection, characterisation, and reporting of breast lesions.
Suicide rates in the U.S. have been rising at an alarming rate over the past few decades, with rates among veterans 1.5 times higher than those of the general public, USC Suzanne Dworak-Peck School of Social Work’s study shows.
Researchers at the Johns Hopkins Kimmel Cancer Center validated an artificial intelligence (AI)-powered blood test that accurately detected liver cancer in people from two geographically and biologically distinct populations and uncovered the underlying biological signals that make the test effective.
WEST ORANGE, N.J., July 29, 2026 /PRNewswire/ — Researchers from RWJBarnabas Health and Rutgers Robert Wood Johnson Medical School found that an artificial intelligence (AI)-enabled early warning system helped identify hospitalized patients at risk of rapid clinical decline sooner, contributing to fewer deaths among high-risk patients.
CorVista’s AI-based pulmonary capillary wedge pressure add-on test represents the third FDA clearance for its cardiopulmonary platform.
A research team at The University of Hong Kong (HKU), has developed ClairS—a deep-learning algorithm that significantly improves the detection of cancer mutations using long-read sequencing. Tested on breast cancer, lung cancer and melanoma cell line datasets, ClairS has demonstrated high accuracy across various cancer types and sequencing conditions.
The movements of a surgeon in a procedure—called “surgical gestures”—can be used to predict patient recovery, according to Cedars-Sinai investigators. Their findings, published in npj Digital Medicine, suggest this technology could help surgeons refine their techniques and improve patient outcomes.
The platform is intended for use by dental professionals, dental service organisations, and imaging centres.
A machine learning tool that analyzes information already captured in a child’s electronic health record helped pediatricians more accurately assess asthma risk in standardized clinical case scenarios, according to a pilot randomized clinical trial led by a Regenstrief Institute researcher.