Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yan Gao, Teena Sharma, Yan Cui
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引用次数: 3

Abstract

Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.

应对生物医学数据不平等的挑战:人工智能视角。
人工智能(AI)和其他数据驱动技术有望改变医疗保健,并赋予精准医疗所必需的预测能力。然而,现有的生物医学数据是开发医学人工智能模型的重要资源和基础,并不能反映人类的多样性。生物医学数据中的低代表性已成为非欧洲人群的一个重大健康风险,人工智能的日益应用为这种健康风险的显现和放大开辟了一条新的途径。在这里,我们回顾了生物医学数据不平等的现状,并提出了一个概念框架来理解其对机器学习的影响。我们还讨论了算法干预的最新进展,以缓解生物医学数据不平等引起的健康差异。最后,我们简要讨论了新发现的种族群体之间数据质量的差异及其对机器学习的潜在影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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