The integration of artificial intelligence and machine learning within healthcare data architectures represents a transformative advancement in modern medicine, enabling unprecedented capabilities in predictive analytics and clinical decision support. AI/ML-Optimized Lakehouses provide a unified framework for managing the explosive growth of healthcare data across disparate systems while maintaining regulatory compliance and data integrity. This article synthesizes quantitative evidence demonstrating the technical performance and clinical impact of these advanced architectures. The framework consolidates heterogeneous healthcare data sources, processes both structured and unstructured clinical information, and enables sophisticated predictive modeling across acute care, chronic disease management, and population health domains. Technical advantages include dramatic improvements in query performance, data integration efficiency, and storage optimization while maintaining stringent security requirements. Clinical applications demonstrate significant improvements in early detection of adverse events, complication forecasting, and resource utilization optimization. Implementation considerations highlight the importance of robust governance frameworks, standardized integration approaches, comprehensive validation protocols, and effective change management strategies. The collective evidence indicates that AI/ML-Optimized Lakehouses provide the essential foundation for transitioning healthcare from reactive to proactive care models, ultimately enhancing patient outcomes and operational efficiency.
Keywords: Artificial Intelligence, Clinical Decision Support, healthcare data architecture, precision medicine, predictive analytics
