Fairness in artificial intelligence‐driven multi‐organ image segmentation - Qing Li; Yizhe Zhang; Longyu Sun; Mengting Sun; Meng Liu; Zian Wang; Qi Wang; Shuo Wang; Chengyan Wang (iRADIOLOGY)
Abstract Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi‐organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi‐organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi‐organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi‐organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi‐organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi‐organ segmentation.