The Future of AI in Healthcare: Adoption Challenges and Solutions

The adoption of artificial intelligence (AI) in the healthcare sector offers an unprecedented opportunity to revolutionize global health systems by addressing growing challenges related to efficiency, accessibility, and quality of care. This systematic review explores the opportunities, challenges, enablers, and barriers associated with AI adoption in healthcare, integrating professional, organizational, and patient perspectives. Through an in-depth analysis of the literature, this work highlights an integrated set of determinants for AI adoption, including technological, economic, regulatory, and cultural aspects, with particular emphasis on barriers such as perceived threats to professional autonomy, privacy concerns, and infrastructural gaps. Additionally, the potential of AI to improve clinical outcomes, optimize resources, and foster more equitable and sustainable healthcare systems is discussed. The review introduces an innovative conceptual framework that explores the interactions between external determinants, such as macroeconomic, technological, and regulatory readiness, and internal factors, including organizational and user readiness. Furthermore, it adds an intermediate dimension, represented by the knowledge proximity between clinical and IT specialists, emphasizing the need for greater integration and mutual understanding to ensure effective AI adoption. This model integrates multidisciplinary perspectives and offers practical recommendations for policymakers, AI providers, and healthcare institutions, distinguishing itself from existing models by focusing on diverse application contexts and scalable solutions. This research bridges the gap between technological development and real-world implementation, providing a foundation for future studies and adoption strategies that address the complexities of the healthcare sector.