Yang Yifan, Lin Mingquan, Zhao Han, Peng Yifan, Huang Furong, Lu Zhiyong
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Department of Computer Science, University of Maryland, College Park USA.
ArXiv. 2024 Feb 13:arXiv:2402.08250v1.
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.
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.
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.
人工智能（AI）系统有潜力变革临床实践，包括提高诊断准确性和手术决策能力，同时降低成本和人力。然而，必须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。此类偏见可能在AI模型开发之前、期间或之后出现，因此理解并解决潜在偏见对于在临床环境中准确可靠地应用AI模型至关重要。为了在模型开发过程中减轻对偏见的担忧，我们调查了生物医学自然语言处理（NLP）或计算机视觉（CV）领域中有关不同去偏方法的近期出版物。然后我们讨论了已应用于生物医学领域以解决偏见的数据扰动和对抗学习等方法。
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月发表的相关文章进行文献检索。然后我们使用宽松的约束条件自动筛选出10041篇文章的结果，并人工检查其余890篇文章的摘要，以确定本综述纳入的55篇文章。参考文献中的其他文章也纳入本综述。我们讨论了每种方法并比较了其优缺点。最后，我们回顾了来自通用领域的其他可应用于生物医学以解决偏见和提高公平性的潜在方法。
生物医学中AI的偏见可能源于多个来源，如数据不足、采样偏差以及使用与健康无关的特征或种族调整算法。现有的侧重于算法的去偏方法可分为分布性方法或算法性方法。分布性方法包括数据增强、数据扰动、数据重新加权方法和联邦学习。算法性方法包括无监督表示学习、对抗学习、解缠表示学习、基于损失的方法和基于因果关系的方法。
