{"title":"医疗保健中的机器学习：与隐私、可解释性和偏见相关的伦理考虑因素","authors":"Thomas Hofweber, Rebecca L. Walker","doi":"10.18043/001c.120562","DOIUrl":null,"url":null,"abstract":"Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":"67 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias\",\"authors\":\"Thomas Hofweber, Rebecca L. Walker\",\"doi\":\"10.18043/001c.120562\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.\",\"PeriodicalId\":39574,\"journal\":{\"name\":\"North Carolina Medical Journal\",\"volume\":\"67 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"North Carolina Medical Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18043/001c.120562\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"North Carolina Medical Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18043/001c.120562","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.
期刊介绍：
NCMJ, the North Carolina Medical Journal, is meant to be read by everyone with an interest in improving the health of North Carolinians. We seek to make the Journal a sounding board for new ideas, new approaches, and new policies that will deliver high quality health care, support healthy choices, and maintain a healthy environment in our state.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
