Enhancing Infection Control in ICUS Through AI: A Literature Review

Abstract

Introduction: Infection control in intensive care units (ICUs) is crucial due to the high risk of healthcare-associated infections (HAIs), which can increase patient morbidity, mortality, and costs. Effective measures such as hand hygiene, use of personal protective equipment (PPE), patient isolation, and environmental cleaning are vital to minimize these risks. The integration of artificial intelligence (AI) offers new opportunities to enhance infection control, from predicting outbreaks to optimizing antimicrobial use, ultimately improving patient safety and care in ICUs. Objectives: The primary objectives are to explore AI's impact on predicting HAIs, real-time monitoring, automated sterilization, resource optimization, and personalized infection control plans. Methodology: A comprehensive search of PubMed and Scopus was conducted for relevant articles up to January 2024, including case series, reports, and cohort studies. Animal studies and irrelevant articles were excluded, with a focus on those considered to have significant clinical relevance. Discussion: The review highlights AI's prowess in predicting HAIs, surpassing conventional methods. Existing evidence demonstrates AI's efficacy in accurately predicting and mitigating HAIs. Real-time patient monitoring and alert systems powered by AI are shown to enhance infection detection and patient outcomes. The paper also addresses AI's role in automating sterilization and disinfection, with studies affirming its effectiveness in reducing infections. AI's resource optimization capabilities are exemplified in ICU settings, showcasing its potential to improve resource allocation efficiency. Furthermore, the review emphasizes AI's personalized approach to infection control post-procedures, elucidating its ability to analyze patient data and create tailored control plans.