Hospital-Associated Infection, Surveillance: Uses of Artificial Intelligence

Hospital-associated infections (HAIs) remain a major global challenge, contributing to prolonged hospital stays, increased healthcare costs, antimicrobial resistance, and avoidable patient morbidity and mortality. Traditional HAI surveillance methods are often labor-intensive, time-consuming, and dependent on delayed laboratory confirmation, limiting timely interventions. This study explores the surveillance uses of artificial intelligence (AI) in preventing and managing HAIs by enabling automated data collection, early detection, and risk prediction. AI-driven models can integrate electronic health records, microbiology results, vital signs, radiology findings, medication history, and unstructured clinical notes through natural language processing to identify high-risk patients and detect infection trends in real time. In addition, AI supports outbreak detection, monitoring of hand hygiene compliance, and antimicrobial stewardship by optimizing antibiotic selection and predicting resistance patterns. Overall, AI-based surveillance systems offer promising opportunities to enhance infection control strategies, improve clinical decision-making, and strengthen hospital safety. However, challenges such as data quality, algorithm transparency, and implementation barriers must be addressed to ensure reliable adoption in real-world healthcare environments.