AI-Augmented Health Data Governance Systems for Predictive Diagnosis, Regulatory Compliance, and Risk Mitigation - Olasehinde Omolayo; Tope David Aduloju; Ajao Ebenezer Taiwo; Babawale Patrick Okare (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
The exponential growth of health data, driven by digitization and the Internet of Medical Things (IoMT), has created unprecedented opportunities and challenges for modern healthcare systems. Artificial Intelligence (AI)-augmented health data governance frameworks are emerging as a strategic response to manage complex data ecosystems, enabling predictive diagnosis, enhancing regulatory compliance, and reducing operational and clinical risks. This review paper explores the integration of AI into health data governance, examining its role in improving data quality, transparency, and decision-making. It evaluates machine learning techniques for predictive analytics, natural language processing for unstructured health data, and rule-based engines for dynamic policy enforcement. The paper also assesses how AI facilitates compliance with frameworks such as HIPAA, GDPR, and FDA guidelines by automating audit trails, anomaly detection, and data access monitoring. Furthermore, it highlights risk mitigation strategies through AI-driven threat intelligence, privacy-preserving computation, and adaptive access controls. By synthesizing current innovations, challenges, and implementation models, this study offers a forward-looking framework for deploying AI in health data governance to achieve resilient, compliant, and patient-centric healthcare delivery systems.