Overview
AI applications in health equity aim to bridge the gap in healthcare access and outcomes for marginalized communities. By leveraging data and advanced algorithms, AI can identify health disparities, predict disease outbreaks, and optimize resource allocation. This technology plays a crucial role in ...
Key Terms
Example: Rural areas often have higher rates of chronic diseases compared to urban areas.
Example: Machine learning algorithms can predict patient outcomes based on historical data.
Example: Predictive analytics can forecast disease outbreaks in specific communities.
Example: Data analysis helps identify trends in patient demographics.
Example: If an AI system is trained on biased data, it may perpetuate existing health disparities.
Example: Ethical considerations in AI include fairness, accountability, and transparency.