(untitled) (The Review of Diabetic Studies, 2024)
Background Traditional infection control relies on manual surveillance, which suffers from underreporting (30-50%), delays, and resource limitations, contributing to 1.7 million annual HAIs in the US alone, with global costs exceeding 30 billion.​ Methods This narrative review synthesizes peer-reviewed evidence from 2015-2026 on automation (e.g., UV robots), digital technologies (e.g., IoT sensors, EHR triggers), and analytics (e.g., AI/ML prediction) sourced from PubMed, Scopus, and Cochrane. RCTs, cohorts, and systematic reviews (n>50) were prioritized, excluding pre-2015 or non-healthcare studies.​ Results Technologies reduced HAIs by 20-40% in ICUs via AI surveillance and UV disinfection (96% microbial kill); IoT improved hand hygiene compliance to 72-85%; predictive ML achieved 90-95% accuracy in early detection. LMIC pilots, including Egypt, showed scalable mobile AI for outbreaks.​ Conclusions Automation, digital tools, and analytics surpass manual methods, enabling proactive HAI prevention despite barriers like legacy systems. Phased adoption with training and policies is essential for equitable, sustainable implementation toward zero-HAI goals.​