Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) pose a significant global health challenge, being the most common adverse events in healthcare settings. Traditional surveillance methods, while essential, are often costly and labor-intensive. AI and ML offer innovative solutions to enhance HAI surveillance, improve patient risk stratification, and predict infections more effectively. Their applications extend to clinical laboratory testing, imaging, and antimicrobial resistance profiling. The COVID-19 pandemic underscored the need for automation and AI-driven solutions to manage rising patient volumes and diagnostic workloads. AI can assist in interpreting lab results, particularly amid shortages of trained personnel, streamlining workflows in clinical microbiology.

With over 2.6 million new HAI cases annually in Europe, AI-driven infection forecasting in hospitals and ICUs is crucial for effective infection control. AI has proven valuable in organizing and implementing preventive strategies, though challenges remain in its integration. The increasing digitization of health records and advances in computing power have further fueled AI-driven research in infectious disease management. Rather than replacing jobs, AI should be seen as an opportunity to improve patient care, optimize staff allocation, reduce healthcare costs, and enhance infection prevention strategies. This review, based on articles from PubMed and Scopus, explores AI’s role in advancing HAI diagnosis and prevention, providing insights into future applications in this emerging field.

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