解决生物医学中人工智能公平性和偏见的最新方法概览

IF 4 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Biomedical Informatics Pub Date : 2024-04-25 DOI:10.1016/j.jbi.2024.104646
Yifan Yang , Mingquan Lin , Han Zhao , Yifan Peng , Furong Huang , Zhiyong Lu
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引用次数: 0

摘要

目标人工智能(AI)系统有可能彻底改变临床实践,包括提高诊断准确性和手术决策,同时降低成本和人力。然而,重要的是要认识到这些系统可能会延续社会不平等或表现出偏见,例如基于种族或性别的偏见。这些偏见可能发生在人工智能模型开发之前、期间或之后,因此了解和解决潜在的偏见至关重要,以便在临床环境中准确可靠地应用人工智能模型。为了减轻模型开发过程中的偏差问题,我们调查了最近在生物医学自然语言处理(NLP)或计算机视觉(CV)领域发表的有关不同去偏差方法的文章。然后,我们讨论了在生物医学领域应用于解决偏差问题的方法,如数据扰动和对抗学习。方法我们在 PubMed、ACM 数字图书馆和 IEEE Xplore 上使用多种关键词组合对 2018 年 1 月至 2023 年 12 月间发表的相关文章进行了文献检索。然后,我们以宽松的限制条件自动过滤了 10041 篇文章,并人工检查了剩余 890 篇文章的摘要,最终确定了纳入本综述的 55 篇文章。参考文献中的其他文章也包含在本综述中。我们讨论了每种方法,并比较了其优缺点。结果生物医学中人工智能的偏差可能来自多个方面,如数据不足、抽样偏差、使用与健康无关的特征或种族调整算法。现有的针对算法的去污方法可分为分布式和算法式两种。分布式方法包括数据增强、数据扰动、数据重权方法和联合学习。算法方法包括无监督表示学习、对抗学习、分离表示学习、基于损失的方法和基于因果关系的方法。
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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A survey of recent methods for addressing AI fairness and bias in biomedicine

Objectives

Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias.

Methods

We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness.

Results

The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.

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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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