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 …

Deep Learning has thrived on the emergence of biomedical big data. However, medical
datasets acquired at different institutions have inherent bias caused by various confounding …

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 …

The recent trend of integrating multi-source Chest X-Ray datasets to improve automated
diagnostics raises concerns that models learn to exploit source-specific correlations to …

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 …

Deep learning models were frequently reported to learn from shortcuts like dataset biases.
As deep learning is playing an increasingly important role in the modern healthcare system …

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 …

Recent advances in training deep learning models have demonstrated the potential to
provide accurate chest X-ray interpretation and increase access to radiology expertise …

Medical imaging machine learning algorithms are usually evaluated on a single dataset.
Although training and testing are performed on different subsets of the dataset, models built …

Purpose Machine learning (ML) algorithms are well known to exhibit variations in prediction
accuracy when provided with imbalanced training sets typically seen in medical imaging …