A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

More than 20 faculty members and several students from across academic disciplines attended a two-day training workshop on June 4–5 to learn how AI machine-learning skills can assist with their ...

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

Managing a medical supply chain in low- and middle-income countries can mean navigating a landscape prone to extreme and ...

The seven companies listed here cover the realistic range of what a buyer will encounter in 2026: embedded ML teams that own ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

Government agencies face increasingly sophisticated security challenges in a world driven by digital transformation.

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...

Ars Technica has been separating the signal from the noise for over 25 years. With our unique combination of technical savvy ...

We independently review everything we recommend. When you buy through our links, we may earn a commission. Learn more› By Matthew Guay After a new round of testing, we found that the best app depends ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...