From Data Privacy to Decision Stability: A Governance Framework for AI-Driven Healthcare

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare by enabling advanced data-driven capabilities in diagnosis, prognosis, operational optimization, and personalized care. These advances rely on large-scale access to sensitive patient data, placing privacy, data security, and ethical governance at the center of responsible AI deployment. While prior research has extensively documented privacy-preserving techniques and regulatory challenges, the ethical implications of AI-driven data use extend beyond technical safeguards to include patient autonomy, trust, and systemic accountability. This article examines the transformative impact of AI in healthcare through the lens of patient data usage, focusing on privacy risks, security mechanisms, and ethical considerations that arise when clinical decision-making is increasingly mediated by algorithms. We synthesize existing privacy-preserving approaches-including encryption, differential privacy, federated learning, and decentralized data governance-and analyze their ethical implications for transparency, bias, and patient agency. By framing privacy and security as foundational enablers of ethical AI rather than secondary constraints, this work highlights the need for integrated governance models that align technological innovation with societal values, regulatory expectations, and patient-centered care.

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