Abstract

The need for semantic interoperability in healthcare has never been more critical as institutions strive to unify disparate data sources while maintaining regulatory compliance and operational efficiency. This paper introduces a multi-layered cloud-based framework designed to enhance semantic interoperability, integrating artificial intelligence (AI), ontology mapping, and federated learning. Utilizing advanced technologies such as AWS Neptune for knowledge graphs, Amazon Comprehend Medical for entity extraction, and Amazon SageMaker for predictive analytics, our approach streamlines healthcare data exchange while preserving security and compliance standards like HIPAA and GDPR. Through real-world implementation across multiple healthcare institutions, our results demonstrate a 91% accuracy in semantic data mapping and an 84% reduction in cross-institutional data retrieval time. This research establishes a scalable, intelligent, and adaptive interoperability architecture that paves the way for AI-driven diagnostics, real-time health analytics, and precision medicine.  Keywords: AI-Powered Data Normalization, Semantic Interoperability, Cloud-Driven Data Engineering, Healthcare Data Integration, Federated Learning, FHIR Compliance, AI-Powered Optimization, Predictive Analytics, Blockchain Securityin Healthcare, Knowledge Graphs   The need for semantic interoperability in healthcare has never been more critical as institutions strive to unify disparate data sources while maintaining regulatory compliance and operational efficiency. This paper introduces a multi-layered cloud-based framework designed to enhance semantic interoperability, integrating artificial intelligence (AI), ontology mapping, and federated learning. Utilizing advanced technologies such as AWS Neptune for knowledge graphs, Amazon Comprehend Medical for entity extraction, and Amazon SageMaker for predictive analytics, our approach streamlines healthcare data exchange while preserving security and compliance standards like HIPAA and GDPR. Through real-world implementation across multiple healthcare institutions, our results demonstrate a 91% accuracy in semantic data mapping and an 84% reduction in cross-institutional data retrieval time. This research establishes a scalable, intelligent, and adaptive interoperability architecture that paves the way for AI-driven diagnostics, real-time health analytics, and precision medicine