From algorithmic performance to ethical governance: a deployment-oriented framework for responsible healthcare AI

Abstract

Artificial intelligence (AI) is increasingly being integrated into healthcare for diagnosis, decision support, and operational optimization, yet real-world deployment remains inconsistent and institutionally challenging. This study presents a critical and integrative review of the ethical, regulatory, technical, and socio-technical issues shaping responsible healthcare AI implementation. Rather than proposing a new algorithm method, the study develops a governance-oriented conceptual synthesis that reframes deployment as a lifecycle integration challenge. Drawing on interdisciplinary literature and global regulatory frameworks, the review identifies recurring barriers, including dataset shift, performance drift, algorithmic bias, accountability gaps, and institutional misalignment. While these challenges are widely recognized, they are often treated as isolated concerns. The central contribution of this study is the proposed Lifecycle-Integrated Healthcare AI Governance Framework (LIHAG), a four-layer deployment-oriented governance model integrating technical robustness, ethical safeguards, regulatory compliance, and human oversight as interdependent dimensions of responsible AI deployment. Through illustrative healthcare use cases, the analysis demonstrates how lifecycle-integrated governance can support ethically aligned, regulation-ready, and institutionally sustainable AI implementation. By shifting evaluative emphasis from predictive accuracy alone to coordinated governance design, this study contributes a structured framework for responsible and sustainable healthcare AI deployment.