USING AI IN INFECTION PREVENTION AND CONTROL

At the 34th European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Global conference in Barcelona, Spain, the role of artificial intelligence (AI) in optimizing infection prevention and control was highlighted. AI encompasses tools like machine learning, which requires human intervention for data extraction, and deep learning, which can independently process large datasets.

In the past five years, nearly 4,000 publications have explored AI-based tools for preventing and controlling healthcare-associated infections (HAIs), a significant increase from the 1,350 studies published between 1976 and 2018.

AI's applications in this field include surveillance, predictive analysis, antimicrobial stewardship, environmental monitoring, personalized patient care, and education and training.

EXAMPLES OF AI IN INFECTION PREVENTION

■ Facial recognition systems to ensure proper face mask use in hospitals.

■ Hand hygiene monitoring systems using convolutional neural networks to detect germs and ensure compliance with hygiene standards.

■ Robots with sensors to efficiently disinfect hospital environments.

■ Network graphs to analyze interactions within hospitals and identify disease transmission patterns.

Despite the expanding body of research, the integration of AI into infection prevention and control is still in its developmental stages. SUCCESSFUL IMPLEMENTATION DEMANDS CLOSE COLLABORATION BETWEEN HEALTHCARE PROVIDERS AND AI SPECIALISTS to bridge the gap between medical expertise and technological innovation. However, several barriers persist, including a lack of comprehensive regulation, limited knowledge of AI's practical applications in healthcare, and concerns about data privacy and ethical use. While AI holds immense potential to revolutionize infection prevention practices, its widespread adoption remains an aspirational goal for the future