Redefining Healthcare IT Governance and Risk Management Through Artificial Intelligence and Machine Learning Innovations

Abstract

As healthcare organizations face increasing regulatory complexity, data security threats, and operational risks, the need for robust IT governance and proactive risk management has become more urgent than ever. Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are redefining the landscape of healthcare IT governance by enabling advanced automation, predictive analytics, and intelligent decision-making.

This study explores the transformative potential of AI and ML in strengthening IT governance and risk management practices within healthcare systems. Through a comprehensive literature review of pre-2023 academic and industry sources, the paper synthesizes key technological advancements, including AI-driven compliance tracking, anomaly detection, and predictive risk modeling. Particular attention is given to how these technologies automate traditionally manual processes, improve auditability, and adapt to dynamic regulatory environments.

Empirical insights are drawn from secondary data and case-based evaluations, illustrating both the practical implementations and limitations of AI/ML solutions in real-world healthcare settings. The findings highlight critical opportunities—such as real-time risk scoring, policy optimization, and automated threat identification—alongside challenges related to data quality, algorithmic transparency, and ethical oversight.

This research contributes to the growing discourse on digital transformation in healthcare by demonstrating how AI and ML can serve as strategic enablers of secure, compliant, and adaptive IT governance frameworks. It also provides actionable guidance for healthcare administrators and IT leaders seeking to align technology deployment with risk mitigation and regulatory goals.