A machine learning–based tool accurately predicted risk for recurrent inflammatory activity after DMT discontinuation in MS, highlighting its potential to guide personalized treatment decisions.

MIT researchers developed an interactive, AI-based system that enables users to rapidly annotate areas of interest in new biomedical imaging datasets, without training a machine-learning model in ...

In a presentation delivered at the 26th Annual International Lung Cancer Congress, Sandip Patel, MD, discussed the rise of AI-enabled imaging and digital pathology in predicting treatment response, ...

A new study shows that routine hospital blood tests could help predict spinal cord injury severity and survival chances.

AI applications are a promising solution for PAD that may translate into earlier detection, customized risk assessment, and improved outcomes.

From CASP to the Virtual Cell Challenge, researchers are leveraging science competitions and high-risk, high-reward grants to ...

CISPA researcher Sarath Sivaprasad, together with Hui-Po Wang and Mario Fritz from CISPA and other colleagues from HIPS, has ...

Machine learning models can help predict which patients receiving systemic therapy for non-small lung cancer are most likely ...

Recent advances in high-throughput microbiome profiling have generated expansive data sets that offer unprecedented ...

Discover how AI and data science are revolutionizing sports, from predicting wins to preventing injuries, in ways coaches can actually use.

This article explores the science behind emerging 3D pregnancy scans and what the future holds for preventing pregnancy ...