A Scalable AI Cloud Architecture Advancing Healthcare Governance Risk Oversight and Digital Trust with Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2024.0605014Keywords:
AI Cloud Architecture, Healthcare Governance, Risk Oversight, Digital Trust, Machine Learning, Cloud Security, Compliance Automation, Healthcare AnalyticsAbstract
The rapid digitalization of healthcare systems has intensified the need for scalable, secure, and intelligent cloud architectures capable of ensuring governance, risk oversight, and digital trust. Traditional healthcare IT infrastructures struggle to manage the growing complexity of data-intensive operations, regulatory compliance, and emerging cyber threats. Artificial Intelligence (AI) and Machine Learning (ML), when integrated into cloud architectures, offer transformative capabilities for automating governance processes, predicting risks, and enhancing trust in digital healthcare ecosystems. This paper proposes a scalable AI-driven cloud architecture designed to advance healthcare governance, strengthen risk oversight, and foster digital trust through intelligent automation. The architecture leverages cloud-native services, machine learning models, and security-by-design principles to support compliance monitoring, anomaly detection, data integrity, and ethical data usage. A comprehensive literature review highlights existing approaches to cloud-based healthcare systems, governance frameworks, and AI-driven risk management, identifying critical gaps in scalability and trust assurance. The proposed research methodology outlines architectural layers, data governance mechanisms, ML workflows, security controls, and evaluation metrics. The study concludes by discussing the advantages and limitations of the proposed architecture, demonstrating its potential to support resilient, transparent, and trustworthy healthcare digital transformation.
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