Navigating Fairness in Radiology AI: Concepts, Consequences,and Crucial
  Considerations
Vasantha Kumar Venugopal Abhishek Gupta Rohit Takhar Charlene Liew Jin Yee Catherine Jones Gilberto Szarf
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.