Navigating Fairness in Radiology AI: Concepts, Consequences,and Crucial Considerations

TL;DR

This paper reviews the importance of fairness in radiology AI, emphasizing bias detection using the Aequitas toolkit and discussing its implications for equitable disease screening outcomes.

Contribution

It introduces the application of the Aequitas bias audit toolkit in radiology AI, highlighting its role in identifying hidden biases and ensuring fairness across diverse demographic groups.

Findings

Aequitas can identify biases across multiple variables simultaneously.

Fairness metrics like False Positive Rate Parity are crucial for equitable disease screening.

Disparities in AI decisions can lead to significant real-world impacts.

Abstract

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.