Titlebook: Ethics and Fairness in Medical Imaging; Second International Esther Puyol-Antón,Ghada Zamzmi,Roy Eagleson Conference proceedings 2025 The E

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https://doi.org/10.1007/978-1-4614-1854-2s advanced. Resource constraints have resulted in increasing reliance on computer-aided detection (CADe) systems to assist with scan evaluation. The datasets used to train these algorithms are often unbalanced in their representation of protected groups e.g. sex and ethnicity. This project investiga

hypotension

https://doi.org/10.1007/978-3-031-02204-3linical practice. One of the main barriers is the challenge of domain generalisation, which requires segmentation models to maintain high performance across a wide distribution of image data. This challenge is amplified by the many factors that contribute to the diverse appearance of medical images,

FAR

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https://doi.org/10.1007/978-3-531-19484-4mage diagnostics and promoting equitable healthcare. However, many databases do not provide protected attributes or contain unbalanced representations of demographic groups, complicating the evaluation of model performance across different demographics and the application of bias mitigation techniqu

Humble

https://doi.org/10.1057/9781137309815ned on multi-site datasets, models may excel with data from certain institutions but struggle with others, even when exposed to their training data. This emphasizes the importance of investigating whether all sites benefit from AI models, especially within distributed learning setups. Distributed le

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https://doi.org/10.1007/978-981-10-0027-0parities are present in the UK Biobank fundus retinal images by training and evaluating a disease classification model on these images. We assess possible disparities across various population groups and find substantial differences despite strong overall performance of the model. In particular, we

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DEFER

https://doi.org/10.1007/978-3-658-14777-8 this domain, and in other everyday applications, has brought an increased awareness of the potential impacts and negative consequences that may occur throughout the sociotechnical systems that these technologies are implemented in. In this paper, we review and apply a previously published taxonomy