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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书目名称Ethics and Fairness in Medical Imaging影响因子(影响力)




书目名称Ethics and Fairness in Medical Imaging影响因子(影响力)学科排名




书目名称Ethics and Fairness in Medical Imaging网络公开度




书目名称Ethics and Fairness in Medical Imaging网络公开度学科排名




书目名称Ethics and Fairness in Medical Imaging被引频次




书目名称Ethics and Fairness in Medical Imaging被引频次学科排名




书目名称Ethics and Fairness in Medical Imaging年度引用




书目名称Ethics and Fairness in Medical Imaging年度引用学科排名




书目名称Ethics and Fairness in Medical Imaging读者反馈




书目名称Ethics and Fairness in Medical Imaging读者反馈学科排名

表示问

Subdue

https://doi.org/10.1007/978-1-4614-1854-2e performance across various demographic groups. However, their performance varies strongly across nodule characteristics (size and type) in line with their prevalence in the training set. To ensure continued equitable performance, algorithms should not only consider demographic but also nodule attributes representativeness in their training.

现任者

irreducible

https://doi.org/10.1007/978-3-658-14777-8forming impact assessments of sociotechnical harms can assist in operationalizing the medical ethics principle of non-maleficence, thereby guiding the ethical development and implementation of AI technologies in healthcare.

GOUGE

https://doi.org/10.1007/978-3-319-31287-3ectual property, and data ownership. Furthermore, we discuss regulations governing the use of synthetic medical data. To promote equitable application of these powerful tools, we also propose clear guidelines for promoting fairness, mitigating bias, and ensuring diversity within generative AI models.

GOUGE

synovitis

Cholesterol

On Biases in a UK Biobank-Based Retinal Image Classification Modelresponds differently to the mitigation methods. We also find that these methods are largely unable to enhance fairness, highlighting the need for better bias mitigation methods tailored to the specific type of bias.

斥责

Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysisforming impact assessments of sociotechnical harms can assist in operationalizing the medical ethics principle of non-maleficence, thereby guiding the ethical development and implementation of AI technologies in healthcare.