[HTML][HTML] Algorithmic encoding of protected characteristics in chest X-ray disease detection models
Background It has been rightfully emphasized that the use of AI for clinical decision making
could amplify health disparities. An algorithm may encode protected characteristics, and …
could amplify health disparities. An algorithm may encode protected characteristics, and …
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Machine learning systems have received much attention recently for their ability to achieve
expert-level performance on clinical tasks, particularly in medical imaging. Here, we …
expert-level performance on clinical tasks, particularly in medical imaging. Here, we …
[HTML][HTML] Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in
medical imaging applications. However, there is growing concern that such AI systems may …
medical imaging applications. However, there is growing concern that such AI systems may …
Predicting patient demographics from chest radiographs with deep learning
Background Deep learning models are increasingly informing medical decision making, for
instance, in the detection of acute intracranial hemorrhage and pulmonary embolism …
instance, in the detection of acute intracranial hemorrhage and pulmonary embolism …
Risk of bias in chest radiography deep learning foundation models
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 …
presence of biases that could lead to subgroup performance disparities across biologic sex …
Debiasing deep chest x-ray classifiers using intra-and post-processing methods
R Marcinkevics, E Ozkan… - Machine Learning for …, 2022 - proceedings.mlr.press
Deep neural networks for image-based screening and computer-aided diagnosis have
achieved expert-level performance on various medical imaging modalities, including chest …
achieved expert-level performance on various medical imaging modalities, including chest …
When more is less: Incorporating additional datasets can hurt performance by introducing spurious correlations
In machine learning, incorporating more data is often seen as a reliable strategy for
improving model performance; this work challenges that notion by demonstrating that the …
improving model performance; this work challenges that notion by demonstrating that the …
Improving the fairness of chest x-ray classifiers
Deep learning models have reached or surpassed human-level performance in the field of
medical imaging, especially in disease diagnosis using chest x-rays. However, prior work …
medical imaging, especially in disease diagnosis using chest x-rays. However, prior work …
CheXpedition: Investigating generalization challenges for translation of chest X-ray algorithms to the clinical setting
Although there have been several recent advances in the application of deep learning
algorithms to chest x-ray interpretation, we identify three major challenges for the translation …
algorithms to chest x-ray interpretation, we identify three major challenges for the translation …
Quantifying and leveraging classification uncertainty for chest radiograph assessment
The interpretation of chest radiographs is an essential task for the detection of thoracic
diseases and abnormalities. However, it is a challenging problem with high inter-rater …
diseases and abnormalities. However, it is a challenging problem with high inter-rater …