Developing models that can answer questions of the form" How would $ x $ change if $ y $
had been $ z $?" is fundamental for advancing medical image analysis. Training causal …

Background Breast density is an important risk factor for breast cancer complemented by a
higher risk of cancers being missed during screening of dense breasts due to reduced …

Purpose To train an explainable deep learning model for patient reidentification in chest
radiograph datasets and assess changes in model-perceived patient identity as a marker for …

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit
bias in the form of disparities in performance between subgroups. Since not all sources of …

Despite significant technical advances in machine learning (ML) over the past several years,
the tangible impact of this technology in healthcare has been limited. This is due not only to …

Purpose Prior studies show convolutional neural networks predicting self-reported race
using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We …

Deep learning (DL) models can harness electronic health records (EHRs) to predict
diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs …

Medical imaging models have been shown to encode information about patient
demographics such as age, race, and sex in their latent representation, raising concerns …

Background Perhaps nowhere else in the healthcare system than in the intensive care unit
environment are the challenges to create useful models with direct time-critical clinical …

Background It has been shown that AI models can learn race on medical images, leading to
algorithmic bias. Our aim in this study was to enhance the fairness of medical image models …