Privacy-Preserving AI Analytics for Healthcare Data Integration across Distributed Computing Platforms

Abstract

The growing adoption of artificial intelligence (AI) in healthcare has created significant opportunities for improving disease prediction, clinical decision-making, population health management, and personalized treatment. However, healthcare data are distributed across hospitals, diagnostic laboratories, research institutions, electronic health record systems, and cloud-based platforms, creating challenges related to interoperability, privacy, security, and regulatory compliance. This study examines privacy-preserving AI analytics for healthcare data integration across distributed computing platforms, emphasizing the integration of federated learning, secure multiparty computation, differential privacy, and encrypted data processing. These technologies enable healthcare organizations to collaboratively analyze distributed datasets while reducing the need to transfer sensitive patient information into centralized repositories. The study adopts a conceptual and analytical research methodology based on relevant literature, architectural principles, and comparative assessment of privacy-preserving techniques. It investigates the relationships between analytical performance, data confidentiality, computational efficiency, interoperability, and governance. The proposed framework integrates distributed data access, privacy-aware model training, secure communication, identity management, and continuous auditing to support trustworthy healthcare analytics. Particular attention is given to challenges involving heterogeneous clinical datasets, communication overhead, model accuracy, privacy leakage, and institutional coordination. The study aims to establish a foundation for secure, scalable, and interoperable healthcare AI systems that support collaborative medical research while maintaining patient confidentiality. The proposed approach also identifies evaluation criteria for future empirical validation across diverse healthcare environments and distributed computing infrastructures.