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Next Generation Healthcare Cloud using AI for Privacy Focused Predictive Modeling and Clinical Intelligence

Abstract

The evolution of healthcare systems toward digital ecosystems has accelerated the adoption of cloud computing and artificial intelligence (AI). This study explores a next-generation healthcare cloud framework that integrates AI-driven predictive modeling with privacy-preserving mechanisms to enhance clinical intelligence. Traditional healthcare systems face challenges such as fragmented data, limited interoperability, and concerns over patient data privacy. The proposed model addresses these issues by leveraging advanced AI techniques, including machine learning and deep learning, within a secure and scalable cloud infrastructure. The framework emphasizes privacy-focused approaches such as data anonymization, federated learning, and secure multi-party computation, ensuring that sensitive patient information is protected while enabling data-driven insights. Predictive modeling capabilities allow early disease detection, risk assessment, and personalized treatment planning, thereby improving patient outcomes and healthcare efficiency. Additionally, the system supports real-time analytics and clinical decision support, enhancing the quality and reliability of care delivery. This research evaluates the proposed model through conceptual analysis and case-based scenarios. The findings demonstrate that integrating AI with privacy-centric cloud systems significantly improves clinical intelligence while maintaining strict data protection standards. The study highlights the importance of balancing innovation with ethical and regulatory considerations in modern healthcare systems.

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