Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

There have been tremendous advances in artificial intelligence (AI) and machine learning
(ML) within the past decade, especially in the application of deep learning to various …

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …

Machine learning is a technique for recognizing patterns that can be applied to medical
images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be …

There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …

Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.
However, undersampling during MRI acquisition as well as the overparameterized and non …

Deep learning (DL) methods have in recent years yielded impressive results in medical
imaging, with the potential to function as clinical aid to radiologists. However, DL models in …

Abstract Machine learning is rapidly gaining importance in radiology. It allows for the
exploitation of patterns in imaging data and in patient records for a more accurate and …

Several sources of bias can affect the performance of machine learning systems used in
medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias …