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

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

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 ...

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

The indictment of a soldier who bet on the U.S. operation to capture President Nicolás Maduro of Venezuela put renewed focus ...

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

A machine learning model analyzing CpG-based DNA methylation accurately predicted the origin of many different cancer types ...

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

More than 40 Democratic lawmakers are pressing the Trump administration to provide guidance underscoring that federal employees cannot use nonpublic information to trade on prediction markets. Led by ...