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.
Department of Population Health Sciences, Weill Cornell Medicine, NY, USA.
J Biomed Inform. 2024 Jun;154:104646. doi: 10.1016/j.jbi.2024.104646. Epub 2024 Apr 25.
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）系统有可能彻底改变临床实践，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，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。这些偏见可能在 AI 模型开发之前、期间或之后发生，因此理解和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。为了减轻模型开发过程中的偏见问题，我们调查了生物医学自然语言处理（NLP）或计算机视觉（CV）领域最近关于不同去偏方法的出版物。然后，我们讨论了已在生物医学领域应用的方法，例如数据扰动和对抗学习，以解决偏见问题。
我们使用多个关键词组合在 PubMed、ACM 数字图书馆和 IEEE Xplore 上进行文献检索，检索了 2018 年 1 月至 2023 年 12 月期间发表的相关文章。然后，我们使用宽松的约束自动过滤了 10041 篇文章的结果，手动检查了其余 890 篇文章的摘要，以确定本综述中包含的 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。最后，我们综述了来自一般领域的其他潜在方法，这些方法可应用于生物医学领域以解决偏见问题并提高公平性。
生物医学中 AI 的偏见可能源于多个来源，例如数据不足、抽样偏差以及使用与健康无关的特征或经过种族调整的算法。侧重于算法的现有去偏方法可分为分布方法和算法方法。分布方法包括数据增强、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。
