Fairness-related performance and explainability effects in deep learning models for brain image analysis

• Published in: Journal of medical imaging (Bellingham, Wash.) Vol. 9; no. 6; p. 061102
• Main Authors: Stanley, Emma A. M., Wilms, Matthias, Mouches, Pauline, Forkert, Nils D.
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.11.2022; SPIE
• Subjects: Artificial intelligence; Brain; Machine learning; Neural networks; Puberty; Special Series on Global Health, Equity, Bias, and Diversity in AI in Medical Imaging; explainable artificial intelligence; fairness; magnetic resonance imaging; machine learning; adolescent brain cognitive development study; bias

Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intel...