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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Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis?ned 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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Cycle-GANs Generated Difference Maps to Interpret Race Prediction from Medical Images in medical images, but what such features may be remains an unanswered question. In this work, we aim to identify image regions relevant to race prediction. We argue that previous methods toward this goal (namely, occlusion maps) are not sufficient as they are unable to locate such regions, and we

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Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis 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

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Practical and Ethical Considerations for Generative AI in Medical Imaging, treatment planning, interventions, and drug development. It benefits the clinical flow with real-time decision-support systems. While generative AI can potentially improve healthcare, it also introduces new ethical issues that require careful analysis and mitigation strategies. This work emphasize