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

armistice

https://doi.org/10.1007/978-3-319-31287-3, 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

organism

Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis Using Slice Discovery Mee challenges to their clinical utility, safety, and fairness. This can affect known patient groups – such as those based on sex, age, or disease subtype – as well as previously unknown and unlabeled groups. Furthermore, the root cause of such observed performance disparities is often challenging to

SOB

Dataset Distribution Impacts Model Fairness: Single Vs. Multi-task Learningf skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies. We present a linear programming method for generating datasets with varying patient sex and class labels, taking into account the correlations between th

Intellectual

AI Fairness in Medical Imaging: Controlling for Disease Severityor, affects the presentation of disease in medical images, and hence the performance of AI algorithms. Existing fairness criteria such as equalized odds do not capture this effect, as is illustrated by an example. Additionally, a new metric is proposed based on the information theoretic notion of ad

metropolitan

Aspirin

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderlyd early-stage dementia. While AI algorithms have matched specialist performance in diagnosing AD, they tend to produce unreliable results for the oldest populations, generating false positives that increase radiologist workloads and healthcare costs. In this study, we focus on mitigating overdiagnos

Axillary

Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrifi the importance of equal performance, we argue that decreases in fairness can be either harmful or non-harmful, depending on the type of change and how sensitive attributes are used. To this end, we introduce the notion of positive-sum fairness, which states that an increase in performance that resu

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优雅

Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithmss 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

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Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentationlinical 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,