Advances in Scalable Data Architectures for AI-Driven Healthcare Analytics

Abstract

Scalable data architectures tailored for AI-driven healthcare analytics are transforming the healthcare landscape by enabling advanced diagnostic capabilities, predictive modeling, and operational optimizations. These architectures address the unique challenges presented by healthcare data, its volume, heterogeneity, quality concerns, and regulatory requirements through innovative combinations of cloud computing, distributed processing frameworks, specialized storage solutions, and sophisticated data pipelines. The progression from traditional monolithic systems to distributed cloud-native architectures has facilitated significant improvements in computational efficiency, data integration, and clinical workflow integration. While substantial progress has been made, persistent challenges include interoperability limitations, compliance-performance tradeoffs, governance complexities, and equity considerations. Emerging architectural trends such as edge-cloud continuum designs, automated optimization, federated analytics, and neuromorphic computing offer promising directions for further advancement. The impact of these architectures extends beyond technical improvements to tangible clinical benefits, including enhanced diagnostic accuracy, earlier intervention capabilities, and substantial operational efficiencies that collectively contribute to improved patient outcomes and healthcare value.