Health Disparity

Health disparities represent inequities in healthcare access and outcomes across different demographic groups, a critical issue driving research focused on identifying and mitigating these biases. Current research utilizes various machine learning models, including logistic regression, gradient boosting machines, random forests, and graph attention networks, to analyze diverse datasets (e.g., electronic health records, satellite imagery, social media data) and uncover the complex interplay of socioeconomic factors, environmental influences, and algorithmic biases contributing to these disparities. Understanding and addressing these disparities is crucial for promoting equitable healthcare delivery and developing fairer, more effective AI-driven tools in medicine and other societal sectors.