CLOUD-ENABLED AI INFRASTRUCTURE IN HEALTHCARE: A SYSTEMATIC REVIEW OF CLINICAL DECISION SUPPORT AND WORKFLOW OPTIMIZATION
Keywords:
Cloud Infrastructure, Healthcare Artificial Intelligence, Clinical Decision Support Systems, Workflow Orchestration, Healthcare InteroperabilityAbstract
The integration of artificial intelligence (AI) and cloud infrastructure is fundamentally transforming healthcare delivery systems, presenting unprecedented opportunities for improving patient care and operational efficiency. This article examines the architectural framework and implementation strategies of cloud-enabled AI systems in healthcare, focusing on clinical decision support systems, workflow orchestration, and operational optimization. The article analyzes how cloud engineering facilitates scalable AI models for diagnostic support, predictive analytics, and medical imaging while enabling sophisticated workflow automation across patient management, hospital operations, and clinical trials. Through case studies from leading healthcare institutions, the article demonstrates these technologies' practical implementation and impact. The article also addresses critical considerations in security compliance, interoperability, and cost-effectiveness of cloud-based healthcare solutions. The article analysis extends to emerging trends, including federated learning, edge computing, and explainable AI, providing insights into the future trajectory of healthcare technology infrastructure. The findings suggest that the convergence of AI and cloud computing creates a robust foundation for next-generation healthcare delivery systems. However, successful implementation requires careful consideration of technical, operational, and regulatory factors.
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