Software engineers are increasingly seeking structured pathways to transition into machine learning roles as companies expand ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the highest and most stable sensitivity, accuracy and discriminatory power, ...

This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

A hybrid AI–human scoring system delivers expert-level accuracy in ulcerative colitis endoscopic assessment while reducing human review by 81 percent.

Researchers at Thomas Jefferson University have developed an automated machine learning (AutoML) model that can accurately ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...