In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

In medical applications, weakly supervised anomaly detection methods are of great interest,
as only image-level annotations are required for training. Current anomaly detection …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in
medical imaging. Materials and Methods Using two large publicly available radiology …

Saliency methods, which produce heat maps that highlight the areas of the medical image
that influence model prediction, are often presented to clinicians as an aid in diagnostic …

Please see the Editorial Comment by Hillary W. Garner discussing this article. Fractures are
common injuries that can be difficult to diagnose, with missed fractures accounting for most …

Since the onset of the COVID-19 pandemic in 2019, many clinical prognostic scoring tools
have been proposed or developed to aid clinicians in the disposition and severity …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Saliency methods are a popular approach for model debugging and explainability.
However, in the absence of ground-truth data for what the correct maps should be …

Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions
of neural networks. Prediction explanation is important, especially in medical imaging, for …