Barriers and limitations for the implementation of Artificial Intelligence in healthcare in Latin America: A Scoping Review
Le résumé fourni par la source
This scoping review aims to systematically map and synthesize the scientific literature on the barriers, limitations, and challenges associated with the implementation of artificial intelligence (AI) and digital transformation technologies in healthcare systems across Latin America between 2016 and 2026. The project is based on the understanding that successful AI adoption in healthcare depends not only on technical model performance, but also on organizational, ethical, regulatory, financial, infrastructural, operational, and sociocultural factors that influence implementation in real-world clinical settings. The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and will be reported according to the PRISMA-ScR guidelines. A comprehensive search strategy will be conducted in multiple databases, including PubMed/MEDLINE, Scopus, Web of Science, Embase, LILACS, SciELO, and BVS, using multilingual search terms in English, Spanish, and Portuguese. Eligible studies will include original research, implementation studies, organizational analyses, and relevant reviews addressing AI adoption, digital transformation, implementation barriers, interoperability, governance, ethics, infrastructure, and operational challenges in healthcare environments within Latin America. The project seeks to identify and classify barriers according to different levels of analysis, including healthcare professionals, institutions, health systems, patients, and regulatory or political environments. It will also characterize the technologies involved, such as machine learning, deep learning, clinical decision support systems, natural language processing, predictive analytics, automation tools, and generative AI applications. Additionally, the review will explore implementation contexts including hospitals, primary care, public health systems, administrative management, and healthcare insurance settings. Expected outcomes include the development of a comprehensive thematic map of barriers to AI implementation in Latin American healthcare systems, identification of research gaps and underrepresented regions or healthcare settings, and a narrative synthesis of the technological, organizational, ethical, and systemic factors affecting digital transformation in the region. The findings are expected to support future implementation science research, institutional strategies, policymaking, and the development of context-sensitive AI adoption frameworks for healthcare systems in Latin America. The project will also provide the foundation for a future strategic white paper directed toward healthcare institutions, policymakers, and health technology startups focused on AI integration in the region.
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