Artificial Intelligence (AI) has significantly revolutionized radiology, promising improved patient outcomes and streamlined processes. However, it's critical to ensure the fairness of AI models to prevent stealthy bias and disparities from leading to unequal outcomes. This review discusses the concept of fairness in AI, focusing on bias auditing using the Aequitas toolkit, and its real-world implications in radiology, particularly in disease screening scenarios. Aequitas, an open-source bias audit toolkit, scrutinizes AI models' decisions, identifying hidden biases that may result in disparities across different demographic groups and imaging equipment brands. This toolkit operates on statistical theories, analyzing a large dataset to reveal a model's fairness. It excels in its versatility to handle various variables simultaneously, especially in a field as diverse as radiology. The review explicates essential fairness metrics: Equal and Proportional Parity, False Positive Rate Parity, False Discovery Rate Parity, False Negative Rate Parity, and False Omission Rate Parity. Each metric serves unique purposes and offers different insights. We present hypothetical scenarios to demonstrate their relevance in disease screening settings, and how disparities can lead to significant real-world impacts.

翻译：人工智能（AI）已显著革新放射学领域，有望改善患者预后并优化诊疗流程。然而，确保AI模型的公平性至关重要，以防止隐蔽的偏见和差异导致不平等的结果。本文综述探讨了AI中的公平性概念，重点聚焦于使用Aequitas工具包进行的偏差审计及其在放射学中的实际应用，特别是在疾病筛查场景中的影响。Aequitas作为一款开源偏差审计工具包，可深入分析AI模型的决策过程，识别可能因不同人口群体和影像设备品牌而引发差异的隐蔽偏见。该工具包基于统计理论运行，通过分析大规模数据集揭示模型的公平性。其优势在于能够同时处理多种变量，尤其在放射学这样多元化的领域中表现突出。本文阐释了关键公平性指标：公平与比例奇偶性、假阳性率奇偶性、假发现率奇偶性、假阴性率奇偶性以及假遗漏率奇偶性。每个指标都具有独特功能并提供不同视角。我们通过假设场景演示这些指标在疾病筛查背景中的相关性，并说明差异如何导致显著的实际影响。