Artificial Intelligence in Infection Control: A Comprehensive Review of Applications and Protocols

Abstract

Hospital-acquired infections (HAIs) continue to pose significant challenges to healthcare systems worldwide, contributing to increased morbidity, mortality, prolonged hospitalization, and healthcare costs. Conventional infection prevention and control (IPC) strategies often rely on labor-intensive surveillance and delayed decision-making, limiting their effectiveness in rapidly evolving clinical environments. Artificial intelligence (AI) has emerged as a transformative technology capable of enhancing infection control through advanced data analytics, machine learning, deep learning, natural language processing, and computer vision. This comprehensive review examines current AI applications in IPC, including early infection detection, healthcare-associated infection prediction, antimicrobial resistance forecasting, automated surveillance, hand hygiene and personal protective equipment compliance monitoring, environmental decontamination, antimicrobial stewardship, and microbiology laboratory support. The review also discusses major implementation challenges, including data quality, interoperability, model interpretability, ethical concerns, organizational readiness, workforce training, and infrastructure limitations, particularly in low-resource settings. Emerging trends such as federated learning, explainable AI, Internet of Things integration, robotics, genomics, and personalized infection prevention are also explored. Overall, AI has considerable potential to strengthen infection prevention by enabling proactive surveillance, improving clinical decision-making, optimizing resource utilization, and supporting real-time interventions. However, successful implementation requires rigorous validation, transparent governance, multidisciplinary collaboration, and integration into existing clinical workflows to ensure safe, equitable, and sustainable adoption.