Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias

Q2 Medicine

North Carolina Medical Journal Pub Date : 2024-07-11 DOI:10.18043/001c.120562

Thomas Hofweber, Rebecca L. Walker

引用次数: 1

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.

查看原文本刊更多论文

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

机器学习模型在医疗应用中大有可为，但也带来了一系列伦理挑战。在本调查中，我们将重点关注训练数据、模型的可解释性和偏差，以及与隐私、自主性和健康公平相关的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

North Carolina Medical Journal Medicine-Medicine (all)

CiteScore

1.40

自引率

0.00%

发文量

121

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.