Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review

Vollständiger Abstract

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Background/Objectives: Chronic diseases place sustained pressure on primary care systems worldwide. While artificial intelligence (AI) shows promise for supporting chronic disease management in primary care, the factors influencing its adoption and implementation have not been comprehensively addressed within the specific context of primary care chronic disease management. The aim of the scoping review was to identify the key drivers that influence AI adoption in primary care settings for chronic disease management. Methods: This scoping review was conducted following the PRISMA ScR guidelines. A comprehensive search of seven databases was conducted between 14 April and 14 May 2025, with an updated search on 27 February 2026 using the same search strategy and eligibility criteria. Eligible studies examined the adoption or implementation of AI for chronic disease management in primary care and were published in English from 2010 to 2026. Data were charted using a standardised extraction tool and synthesised narratively. The protocol for this scoping review was registered with INPLASY. Results: Twenty-two studies were included, identifying five domains influencing AI adoption. Technical factors included data quality, interoperability, and algorithm transparency. Human factors related to clinician trust, workload, digital literacy, and preferences for hybrid human–AI care. Legal and ethical considerations centred on privacy, accountability, fairness, and independent validation. Organisational factors involved leadership, workflow integration, and resourcing, while geographical and cultural contexts shaped readiness, acceptability, and equity. Conclusions: AI adoption in chronic disease management within primary care is shaped by multi-level, context-dependent factors. The findings suggest that implementation may require coordinated strategies encompassing robust data governance and interoperability, clear regulatory frameworks, workforce capability development, workflow-aligned hybrid models with appropriate human oversight, and sustainable resourcing and reimbursement. Future research should extend beyond technical performance to evaluate acceptability, safety, workload, equity, and longer-term organisational impacts.