Artificial Intelligence Bias in Medical Systems: Analysis, Risk Assessment, and Mitigation

Identify, assess, and mitigate AI bias in medical imaging

AI-based predictive algorithms in medical systems produce outcomes skewed by sex, age, race, ethnicity, and insurance status, introducing risk of bias during preprocessing, in-processing, and post-processing stages. Artificial Intelligence Bias in Medical Systems: Analysis, Risk Assessment, and Mitigation systematically traces these bias sources and provides structured approaches for detection and reduction before clinical deployment, through the contributions of a team of researchers in AI, engineering, and medical imaging.

The book examines structured and unstructured medical data limitations, advanced deep learning, and ensemble-based AI techniques for cardiovascular risk prediction using IVUS and bias in X-ray, MRI, and CT-based diagnosis of brain tumors and Parkinson's disease. It analyzes machine learning, deep neural networks, and advanced deep learning models while addressing mitigation strategies including explainability, pruning, and data regulatory provisions.

The book also provides:

• Comparative analysis of current AI models including machine learning, deep neural networks, and advanced deep learning applied to medical imaging developments
• Risk of bias evaluation frameworks for algorithms used in cardiovascular disorder, Parkinson's disease, lung cancer, and COVID-19 risk assessment
• Segmentation methods and data collection strategies analyzed for their contribution to AI bias across multiple biomedical imaging modalities
• Legal and ethical analysis covering data ownership, usage and modification rules, data sharing provisions, and automated decision-making governance
• Mitigation techniques such as explainability and pruning designed to reduce bias and model computational expense before clinical deployment of AI models

Artificial Intelligence Bias in Medical Systems serves healthcare professionals, medical administrators, data scientists, policymakers, and medical lawyers who need to understand how algorithmic bias affects clinical outcomes. By connecting bias analysis with concrete mitigation strategies and regulatory frameworks, this book provides the technical and governance foundations required for responsible AI deployment in medicine.