Scalable Cloud Intelligence for Preventive and Personalized Healthcare

Abstract

The potential of integrating scalable-cloud intelligence in healthcare systems provides a revolutionary channel of foreseeing, averting, and customized health services. This paper presents a single, cloud-based system with the use of advanced analytics, machine learning, and interoperable data systems that can help transform the way healthcare is delivered. The study aims at integrating the heterogeneous health data, electronic health records (EHRs), wearable IoT devices, medical imaging and genomic datasets in a secure and FHIR-compliant cloud ecosystem. The system can provide real-time predictive insights to clinicians and adaptive and patient-centered care paths using distributed and privacy-preserving machine learning pipelines. Deep-learning-based predictive preventive analytics with time-series modeling is used to detect diseases early and reduce risks, whereas reinforcement learning is used to have personalized treatment plans dynamically. Assessable and explicable AI models are incorporated to improve the aspects of transparency, accountability, and clinical trust. The research also creates a governance and compliance structure that follows the provisions of HIPAA and GDPR, which will result in responsible AI implementation. The advantages of prediction accuracy, model fairness, and efficiency in computation have been proven by experimental benchmarks that prove the possibility to deploy resilient, elastic, and ethically sound health intelligence systems. Finally, the vision of democratized, data-driven healthcare promoted using a secure, scalable, intelligent cloud infrastructure is fostered by this piece.