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Artificial intelligence use and performance in detecting and predicting healthcare-associated infections: A systematic review

Year: 2025

Type: review

Abstract: The increasing digitisation of healthcare data and the rapid development of Artificial Intelligence (AI) pave the way for innovative strategies for infectious disease management. This study aimed to systematically retrieve and summarize current evidence on the use and performance of AI-based models for healthcare-associated infection (HAI) detection (i.e., identifying infections already present in available data) and prediction (i.e., estimating future risk based on earlier patient information). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies published between 1 July 2018 and 12 February 2024. Primary outcomes included technical performance metrics for HAI detection and prediction (e.g. recall, precision, AUROC). Any reported clinical, organisational or economic impacts were evaluated as secondary outcomes. Of 4489 records initially identified, 121 studies were included. Twenty-five studies (20.6 %) focused on HAI detection, with more than half achieving an AUROC above 0.90. In contrast, studies on HAI prediction ( n = 93, 76.9 %) reported more heterogeneous performance. Among studies comparing AI with traditional methods ( n = 32), AI models outperformed conventional approaches in 81.3 % of cases ( n = 26). A growing body of evidence suggests that AI models are equal to or superior to traditional methods for HAI detection and prediction, but challenges remain in evaluating performance, with many studies lacking comparators, few prospective evaluations, and limited assessment of organisational impact. • We observed a significant increase in the number of published studies since 2018 • AI models appear to be equal or superior to traditional methods in HAI control • Overall, AI models show high sensibility and specificity, but low precision • Detection models generally outperform prediction models in terms of AUROC • Many studies lack comparators and prospective assessment of organisational impact

Source: Artificial Intelligence in Medicine

DOI: 10.1016/j.artmed.2025.103321