Mitigating Data Bias in Healthcare AI: Strategies and Impact on Patient Outcomes

Abstract

Data bias in healthcare artificial intelligence (AI) models poses a significant challenge to equitable and accurate patient care. This study explores strategies for mitigating data bias in healthcare AI systems and investigates the resulting impact on patient outcomes. Through a comprehensive analysis of existing bias detection and reduction techniques, as well as fairness-enhancing algorithms, this research aims to shed light on effective approaches for creating more balanced and unbiased AI models. By examining real-world case studies and evaluating the implications of bias reduction, this study provides insights into how addressing data bias can lead to improved patient care, diagnosis accuracy, and treatment recommendations. The findings underscore the critical role that bias mitigation plays in promoting fairness, ethics, and quality in healthcare AI applications, emphasizing the importance of on-going efforts to enhance the accuracy and reliability of AI-driven healthcare solutions.