Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

The prevalence of infections amongst intensive care unit (ICU) patients is inevitably high, and the ICU is considered the epicenter for the spread of multidrug-resistant bacteria. Multiple studies have focused on the microbial diversity largely inhabiting ICUs that continues to flourish despite treatment with various antibiotics, investigating the factors that influence the spread of these pathogens, with the aim of implementing sufficient monitoring and infection control methods. Despite joint efforts from healthcare providers and policymakers, ICUs remain a hub for healthcare-associated infections. While persistence is a unique strategy used by these pathogens, multiple other factors can lead to persistent infections and antimicrobial tolerance in the ICU. Despite the recognition of the detrimental effects biofilm-producing pathogens have on ICU patients, overcoming biofilm formation in ICUs continues to be a challenge. This review focuses on various facets of ICUs that may contribute to and/or enhance biofilm production. A comprehensive survey of the literature reveals the apparent need for additional molecular studies to assist in understanding the relationship between biofilm regulation and the adaptive behavior of pathogens in the ICU environment. A better understanding of the interplay between biofilm production and antibiotic resistance within the environmental cues exhibited particularly by the ICU may also reveal ways to limit biofilm production and indivertibly control the spread of antibiotic-resistant pathogens in ICUs.