Cloud Powered Intelligent Ecosystems for Secure Healthcare Analytics with Artificial Intelligence Driven Innovation
DOI:
https://doi.org/10.21590/Keywords:
Cloud computing, healthcare analytics, artificial intelligence, secure ecosystems, predictive diagnostics, federated learning, data privacy, machine learning, digital health, intelligent healthcare systemsAbstract
The healthcare industry is undergoing a profound digital transformation driven by cloud computing and artificial intelligence (AI). Cloud-powered intelligent ecosystems are emerging as a foundational architecture for secure healthcare analytics, enabling scalable storage, real-time processing, and advanced decision-making capabilities. These ecosystems integrate AI-driven innovation to enhance predictive diagnostics, personalized treatment planning, and operational efficiency in healthcare systems. However, the sensitive nature of medical data introduces significant challenges related to privacy, security, interoperability, and regulatory compliance. This research explores the design and implementation of secure cloud-based healthcare analytics frameworks that leverage AI to improve data-driven healthcare outcomes while ensuring robust data protection. The study emphasizes the use of machine learning for predictive analytics, anomaly detection for cybersecurity, and federated learning for privacy-preserving model training. Additionally, it examines how cloud ecosystems facilitate interoperability among healthcare providers, enabling seamless data sharing and collaboration. Despite these advantages, challenges such as data breaches, algorithmic bias, and infrastructure complexity persist. The proposed framework aims to address these limitations by integrating adaptive security mechanisms and intelligent analytics pipelines. Ultimately, this research contributes to the development of next-generation healthcare systems that are secure, intelligent, and capable of delivering high-quality patient care through cloud and AI integration
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