Enhancing Infection Control in ICUS Through AI: A Literature Review

Area of Science:

• Infectious Diseases
• Medical Informatics
• Intensive Care Medicine

Background:

• Intensive care units (ICUs) face high risks of healthcare-associated infections (HAIs), leading to increased patient morbidity, mortality, and healthcare costs.
• Traditional infection control measures include hand hygiene, personal protective equipment (PPE), patient isolation, and environmental cleaning.
• Artificial intelligence (AI) presents novel opportunities to augment existing infection control strategies in ICUs.

Purpose of the Study:

• To investigate the impact of AI on predicting HAIs.
• To evaluate AI's role in real-time patient monitoring and automated sterilization.
• To explore AI's capabilities in resource optimization and personalized infection control plans.

Main Methods:

• A comprehensive literature search was performed on PubMed and Scopus databases up to January 2024.
• Included studies were case series, reports, and cohort studies with clinical relevance.
• Excluded were animal studies and irrelevant articles.

Main Results:

• AI demonstrates superior performance in predicting HAIs compared to conventional methods.
• AI-powered real-time monitoring systems improve infection detection and patient outcomes.
• AI contributes to automated sterilization, resource optimization, and personalized post-procedure infection control plans.

Conclusions:

• AI offers significant advancements in predicting and mitigating HAIs within ICUs.
• AI integration enhances patient safety through improved monitoring, resource allocation, and tailored infection control.
• AI-driven solutions are pivotal for the future of infection prevention in critical care settings.