CureMetrix

Early signs of one of the most common reproductive cancers may now be spotted with near-perfect accuracy, thanks to a groundbreaking artificial intelligence model. For years, diagnosing endometrial cancer required invasive procedures,

Investigators employed artificial intelligence to characterize tissue samples of high-risk NMIBC that were likely to recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG).

In what's expected to soon be commonplace, artificial intelligence is being harnessed to pick up signs of cancer more accurately than the trained human eye. This latest AI model has a near 100% success rate and serves as a clear sign of things to come.

Experts say the approach could also save millions of pounds by cutting projects or clinical trials that would otherwise fail.

A Boston drug discovery company that uses AI has hit unicorn valuation. It uses transformer diffusion models, which its CEO says can "understand the concept of time and how you change, from cradle to grave.

This review discusses the current state of multicancer early detection tests, the role of machine learning in their development, and their implications for oncology practice and patient care.

Medical errors are the third leading cause of death in the U.S., contributing to over 250,000 preventable deaths each year. NeuralCure AI is tackling this crisis head-on by eliminating diagnostic blind spots and accelerating treatment precision with its cutting-edge AI-driven decision support system.

A research team at UCLA has developed a deep learning-powered chemiluminescence vertical flow assay (CL-VFA) that brings clinical laboratory-grade cardiac troponin I (cTnI) testing to a portable, cost-effective point-of-care platform.