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RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features
Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s).
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
However, the severity and nature of the bias varies between the models, highlighting the importance of model choice when attempting to train fair AI-based segmentation models for medical imaging tasks.
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation.
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