The Role of Artificial Intelligence in Predicting and Preventing Outbreaks of Hospital-Acquired Infections

Hospital-acquired infections (HAIs) represent a serious threat to patient safety and the standard of healthcare, leading to increased morbidity, longer hospital stays, and more healthcare costs. With an emphasis on AI methods that recognize infection trends and evaluate risk variables linked to HAIs, this study reviews recent research on the use of AI in predicting and preventing HAI outbreaks. The findings indicate that AI models have a lot of potential for early outbreak identification, with predicted accuracy outperforming conventional statistical techniques. Prompt alerts and actions including AI-driven technologies for real-time monitoring and infection prediction are essential for lowering HAI incidence rates. However, while AI presents valuable opportunities, its effective application will necessitate resolving operational, ethical, and technical issues that may arise.