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 …

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 …

Although recent scientific studies suggest that artificial intelligence (AI) could provide value
in many radiology applications, much of the hard engineering work required to consistently …

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …

The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology
continues to accelerate, and numerous algorithms are becoming available for use in clinical …

Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be
practiced in the near future, but several issues need to be resolved before AI can be widely …

This Editorial comment refers to the article “Medical students' attitude towards artificial
intelligence: a multicenter survey,” Pinto Dos Santos D, et al Eur Radiol 2018. Key Points• …

The development and integration of machine learning/artificial intelligence into routine
clinical practice will significantly alter the current practice of radiology. Changes in …

OBJECTIVE. Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the
most talked about and controversial topics in radiology and medicine. Over the past few …

To date, widely generalizable artificial intelligence (AI) programs for medical image analysis
have not been demonstrated, including for mammography. Rather than pursuing a strategy …