Author(s) :

Divya Sen.

Volume/Issue :

Abstract :

Artificial Intelligence (AI) has emerged as a transformative technology in healthcare, offering capabilities ranging from enhanced diagnostics and personalized treatment plans to optimized operational workflows. Despite its potential, the widespread adoption of AI in healthcare settings remains limited and uneven. This paper investigates the multifaceted causes of the AI adoption gap in healthcare, emphasizing the complex interplay of technological, organizational, ethical, and regulatory barriers that impede full-scale implementation. It reviews comprehensive implementation frameworks that guide healthcare institutions through the adoption process, focusing on strategies for stakeholder engagement, workflow integration, data governance, and ethical compliance. Through detailed analysis of real-world case studies, the paper illustrates successful deployment approaches, highlighting lessons learned and best practices. The findings aim to provide healthcare leaders, clinicians, and policymakers with actionable insights to bridge the gap between AI innovation and its practical application, ultimately fostering improved patient outcomes and healthcare efficiency.

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