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

Background: Healthcare organizations are increasingly integrating artificial intelligence (AI) to bolster clinical decision-making, enhance operational efficiency, and secure strategic advantages. Despite rapid technological advancements, extant literature on human-AI collaboration in healthcare inadequately addresses the intersection of strategic management theories, user-centered design principles, interpretability, and ethical imperatives. This protocol delineates a scoping review designed to map the multifaceted landscape of AI adoption, thereby establishing a robust foundation for further inquiry. Objectives: This scoping review protocol aims to systematically map the existing body of evidence on AI adoption and utilization in resource-constrained healthcare settings, with a particular focus on how AI supports decision-making, knowledge exchange, and overall organizational performance. It further seeks to identify and critically evaluate the theoretical frameworks that have been employed in these studies, scrutinizing their relevance to strategic management practices and human-AI collaboration. Additionally, the review will analyze the barriers and enablers influencing AI adoption, propose strategic management insights, and highlight theoretical gaps to inform future research directions that address user-centered design, interpretability, and the ethical dimensions of AI-driven health services. Eligibility Criteria: The review will include studies investigating AI-enabled interventions (e.g., machine learning, deep learning, natural language processing) in resource-constrained healthcare settings, irrespective of publication date, provided they are available in English. This approach is intended to capture a comprehensive snapshot of the evolving literature. Sources of Evidence and Methods: A systematic search will be conducted across major databases, including SCOPUS, PubMed, and EBSCOhost-Web of Science, in accordance with the PRISMA-ScR guidelines. An iterative search process will be refined through pilot testing of search strings, and data will be extracted using a pre-specified framework in Covidence. NVivo will be employed to facilitate the thematic synthesis of key analytical categories, including decision-making support, knowledge exchange, operational efficiency, and strategic management dimensions of AI adoption. Expected Outcomes: This review is expected to elucidate the multifaceted roles of AI in resource-constrained healthcare settings, reveal limitations within current theoretical frameworks, and provide strategic insights for future research and practice. By integrating perspectives from strategic management and human-AI collaboration, the review aims to contribute toward more sustainable, interpretable, and ethically grounded AI implementations in healthcare.