Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.

Objective: To examine how artificial intelligence (AI) has been applied to infection prevention and control in healthcare, identify barriers and risks affecting implementation, and develop a structured checklist to support safe adoption. Design: Scoping review conducted in line with Joanna Briggs Institute methodology and reported according to PRISMA-ScR. Methods: PubMed, Scopus, and Web of Science were searched for primary studies (2014-2024) describing real-world AI applications for IPC. Studies reporting implementation experiences, outcomes, or risks were included. Data on study design, AI type, IPC function, integration level, barriers, and outcomes were extracted and synthesized thematically to derive a 41-item decision-support checklist. Results: Of 2,143 records screened, 100 studies met inclusion. Most were published since 2022, with the United States and China leading output. Machine learning dominated (75%), mainly for predictive analytics (53%), HAI detection (13%), and hand hygiene monitoring (13%). Only 15% of tools were integrated into existing digital infrastructures. Barriers centred on data quality (45%), technical and data related (16%), and economic/technical constraints (16%). Reported risks clustered around operational failures (35%), technical errors (33%), and data security (12%). Evidence was heavily skewed toward high-income countries, with limited prospective validation or implementation science. Conclusions: AI offers clear promise for IPC, particularly in early detection and compliance monitoring, but its translation into practice remains constrained by data fragmentation, limited integration, and uneven readiness across settings. Our evidence-informed checklist provides IPC teams with a structured tool to assess feasibility, governance, and resource needs before adoption, supporting safer and sustainable innovation.