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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書目名稱Ethics and Fairness in Medical Imaging影響因子(影響力)




書目名稱Ethics and Fairness in Medical Imaging影響因子(影響力)學科排名




書目名稱Ethics and Fairness in Medical Imaging網(wǎng)絡公開度




書目名稱Ethics and Fairness in Medical Imaging網(wǎng)絡公開度學科排名




書目名稱Ethics and Fairness in Medical Imaging被引頻次




書目名稱Ethics and Fairness in Medical Imaging被引頻次學科排名




書目名稱Ethics and Fairness in Medical Imaging年度引用




書目名稱Ethics and Fairness in Medical Imaging年度引用學科排名




書目名稱Ethics and Fairness in Medical Imaging讀者反饋




書目名稱Ethics and Fairness in Medical Imaging讀者反饋學科排名

表示問

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.

現(xiàn)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.

斥責

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.