For centuries, humans looked to seers and astrologers to determine fate. Today, we look to algorithms, and the loss of agency ...

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

This study highlights the potential for using deep learning methods on longitudinal health data from both primary and ...

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center ...

Head and neck cancers represent a biologically heterogeneous group of malignancies requiring accurate diagnosis, staging, and risk stratification for ...

"This Earth Day, we are reminded that solid evidence is the foundation of effective action to protect our planet." ...

Machine learning models can predict the risk for developing moderate-to-severe persistent asthma and allergic rhinitis in ...

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

A model integrating deep learning with clinical and epidemiologic data may significantly improve lung cancer risk prediction based on LDCT screening.