Integrating Governance Frameworks with Generative AI in Healthcare: Transforming Efficiency, Ethics, and Outcomes

Abstract

The rapid integration of generative AI into healthcare systems has transformed service delivery, improved diagnostic accuracy, accelerated drug discovery, and enabled personalized therapies. However, challenges related to data privacy, bias, and technical and regulatory barriers hinder its full potential. This review explores the applications of generative AI in healthcare, from synthetic data generation to clinical training. Furthermore, the study examines the governance frameworks that impact organizational performance in healthcare organizations, with a focus on transparency, accountability, and the interplay between IT governance and hospital performance. Governance frameworks, such as the Technology Acceptance Model (TAM) and the Non-Adoption-Abandonment-Scale-Scale-Sustain (NASSS) model, provide structured approaches to ensure ethical, transparent, and sustainable adoption of generative AI.