Leveraging Federated Learning and Explainable AI for Advancing Health Equity: A Comprehensive Approach to Reducing Disparities in Healthcare Access and Outcomes

Abstract

This research paper explores an innovative framework that combines Federated Learning (FL) and Explainable Artificial Intelligence (XAI) to address health disparities and promote equity in healthcare access and outcomes. Federated Learning, a decentralized machine learning approach, allows collaborative model training across multiple healthcare entities without compromising patient privacy. This study employs FL to amalgamate diverse health data from underserved populations, ensuring that AI-driven health solutions are inclusive and representative. Simultaneously, the integration of XAI enhances transparency and trust in AI models by providing clear, interpretable insights into model decision-making processes. By applying this integrated framework, the research addresses critical barriers to health equity, such as biases in AI algorithms and unequal resource distribution. The effectiveness of this approach is evaluated through case studies focusing on chronic disease management and personalized treatment plans in marginalized communities. Results demonstrate significant improvements in both the performance of predictive health models and the equitable distribution of healthcare resources. Moreover, the paper discusses the socio-ethical implications of deploying AI in healthcare, emphasizing the importance of culturally sensitive, patient-centered design in technological solutions. This comprehensive approach not only advances the technical frontiers of AI in healthcare but also provides a strategic pathway for policymakers and healthcare providers to systematically reduce healthcare disparities and achieve greater equity in health outcomes.