International Journal of Scientific Research and Engineering Development

Healthcare data management is entering a new era characterized by the convergence of artificial intelligence (AI), data interoperability, and large-scale analytics. Traditional data warehouses, while reliable for structured information, struggle to accommodate unstructured, streaming, and multimodal datasets standard in modern healthcare. The AI-Native Healthcare Lakehouse merges the elasticity of data lakes with the consistency and performance of warehouses while embedding AI at the core of its operations. This paper proposes an architecture that enables integrated analytics, semantic reasoning, and real-time inference on heterogeneous healthcare data. We evaluate its scalability, compliance, and ethical implications across multiple datasets and discuss its role in advancing evidence-based care and digital health transformation.