Artificial intelligence adoption in healthcare: a systematic review of implementation challenges and health services implications

Abstract 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. As a result, the study develops an evidence-informed integrative 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 highlights recurrent knowledge-alignment and coordination challenges between clinical and IT specialists (hereafter referred to as Knowledge-proximity), emphasizing the need for greater integration and mutual understanding to support effective AI implementation. This integrative framework synthesizes multidisciplinary perspectives and provides actionable implications for policymakers, AI providers, and healthcare institutions, with relevance across diverse healthcare contexts and stages of implementation. This research bridges the gap between technological development and real-world implementation, providing a foundation for future studies and evidence-informed strategies to support AI adoption in health services.