Strategic Integration of Artificial Intelligence in Healthcare: Theoretical Frameworks, Adoption, Enablers, and Barriers — A Scoping Review

Abstract

Healthcare organizations increasingly leverage Artificial Intelligence (AI) to enhance clinical decision-making, operational efficiency, and strategic positioning. However, existing research on human-AI collaboration in healthcare has not fully explored how strategic management theories intersect with us-er-centered design principles, interpretability, and ethical considerations essential for building reliable AI partners. This scoping review aimed to (i) map the current landscape of AI-enabled knowledge sharing in healthcare organizations; (ii) identify theoretical frameworks, including human-in-the-loop and user acceptance models; (iii) examine both organizational and user-level enablers and barriers, such as ethical concerns, transparency, and digital literacy; and (iv) propose an integrated strategic management perspective for more robust, inclusive, and ethically grounded AI adoption. Eligible studies addressed AI-enabled interventions (e.g., ma-chine learning, deep learning, natural language processing) in diverse healthcare settings (resource-limited, public, and private institutions), with no date restrictions. Only English-language publications were included. A comprehensive search across SCOPUS, PubMed, and EB-SCOhost-Web of Science yielded 327 articles, with 297 screened for relevance using Covidence software. Five studies met eligibility criteria and were thematically synthesized using NVivo. Analytical categories spanned organizational readiness, stakeholder acceptance, digital/ethical infrastructure, and collaborative AI design features. Key facilitators of successful AI adoption included leadership endorsement, specialized training, robust institutional sup-port, and perceived utility of AI solutions. Frequently em-ployed frameworks (Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, Diffusion of Innovations, Sociotechnical Systems) addressed individual-level behaviors but rarely accounted for deeper strategic management factors. Ethical concerns related to patient privacy, data security, and algorithmic bias underscored the need for transparent and explainable AI, particularly in high-stakes healthcare contexts. Current research on healthcare AI adoption predominantly emphasizes user acceptance without fully integrating strategic management and collaborative design principles. Future inquiry should incorporate human-in-the-loop approaches, interpretability methodologies, and strategic management theories to enhance AI sustainability and transparency, foster trust, and safeguard ethical standards. By coupling these dimensions with organizational strategy, healthcare systems can more effectively harness AI for sustainable competitive advantage, elevated clinical outcomes, and responsible innovation.

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