At first glance, it looks like the start of a human pregnancy: A ball-shaped embryo presses into the lining of the uterus ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

The SleepFM model reveals how sleep analysis can predict disease risk, offering insights into sleep's role as a vital health ...

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

New research explores how artificial intelligence foundation models trained on electronic health record data can be tested to ...

As organizations pour billions of dollars into GenAI projects, they encounter an unfortunate reality: across industry, 95% of organizations fail to achieve any measurable impact from GenAI. 1 Yet ...

AI data trainer roles have moved from obscure contractor gigs to a visible career path with clear pay bands and defined ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Objectives In patients with chronic obstructive pulmonary disease (COPD), severe exacerbations (ECOPDs) impose significant morbidity and mortality. Current guidelines emphasise using ECOPD history to ...