Healthcare-associated infections (HAIs) represent a global public health issue characterized by high incidence rates and severe consequences. They significantly prolong hospital stays, increase risks of excess mortality and disability, exacerbate antimicrobial resistance, and impose a substantial burden on patients’ families and society. Surveillance is the cornerstone of effective HAI prevention and control. Conventional manual surveillance is not only labor-intensive and costly but also lacks standardization. With its powerful data processing and analytical capabilities, artificial intelligence (AI) can significantly reduce the incidence of HAIs, improve patient outcomes, decrease workload, and save costs, offering a new approach for cost-effective and efficient HAI surveillance. This review elaborates on advances in the application of AI in common types of HAIs including sepsis, hospital-acquired pneumonia, urinary tract infections, as well as hand hygiene monitoring, so as to promote the development and implementation of AI in the field of HAI prevention and control.
