Editor: In the November 2023 issue of Radiology: Artificial Intelligence, Dr Glocker and
colleagues report that a deep learning model for chest radiograph interpretation depicted …

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 …

Artificial intelligence (AI) deep learning algorithms promise to aid radiologists in disease
detection and prediction. Extensive efforts are being made to create, train, and test AI …

Editor: We read with interest the article by Dr Bennani and colleagues (1) exploring the
impact of artificial intelligence (AI) assistance for the interpretation of chest radiographs …

Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …

Editor: In the recently published Radiology: Artificial Intelligence article,“RadImageNet: An
Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning (1),” Dr …

Detecting misleading patterns in automated diagnostic assistance systems, such as those
powered by Artificial Intelligence, is critical to ensuring their reliability, particularly in …

Editor: We read with great interest the retrospective study by Dr Khader and colleagues (1),
published in the October 2023 issue of Radiology. Their transformer-based deep learning …

It is our great pleasure to write this commentary on the excellent article by Tejani et al (1) on
understanding and mitigating unwanted bias in artificial intelligence (AI) tools applied to …

Despite the expert-level performance of AI models for various medical imaging tasks, real-
world performance failures with disparate outputs for various subgroups limit the usefulness …