Global adoption and readiness for artificial intelligence (AI) and digital health technologies in infection prevention and control (IPC) : a WHO global survey

Abstract

Background Artificial intelligence and digital health technologies may strengthen Infection Prevention and Control through enhanced surveillance, decision-making, and workforce training. However, global evidence on their uptake and perceived usefulness among IPC professionals remains limited. Methods This cross-sectional online survey coordinated by the World Health Organization between 21 March and 17 April 2025 assessed awareness, adoption, perceived usefulness, barriers, and facilitators related to AI-enabled and digital technologies in IPC. Descriptive analyses used item-specific denominators. Technology use analyses were restricted to current users, while perception-based analyses included all respondents answering each item. Likert-scale usefulness items were analysed as ordinal variables using medians and interquartile ranges (IQRs). Results Of 444 responses received across the six WHO regions, 441 were valid for analysis. Most respondents reported some familiarity with AI and digital technologies, although only 24.5% reported current use and 6.8% reported formal training. Among current users, the most frequently reported technologies were AI-based analysis for IPC research, semi-automatic surveillance systems, AI-supported teaching tools, and AI-based antimicrobial resistance detection tools. Perceived usefulness was generally favourable, with a median score of 4 (IQR 2) for eight of nine assessed applications; environmental disinfection scored lower (median 3, IQR 3). The main reported barriers were lack of training and skills (74.4%), limited funding (71.4%), and integration challenges with existing systems (43.3%). Conclusions This global IPC survey identified a substantial gap between awareness and routine implementation of AI-enabled and digital technologies. Adoption remains limited despite favourable perceptions, highlighting workforce capacity, funding, and system integration as the principal implementation constraints.