ABSTRACT

The increasing risk of antimicrobial resistance (AMR) is directly associated with the prevalent utilization of empirical antimicrobials in emergency departments (EDs), characterized by diagnostic ambiguity and significant time constraints. Artificial intelligence-integrated clinical decision support systems (AI-CDSS), are a new and scalable way to improve antimicrobial stewardship by making it easier to make quick, data-driven decisions. This brief communication examines the latest developments in AI-CDSS for infection management in emergency departments, focusing on model types, integration with electronic health records, and documented effects on antimicrobial prescribing. Even while the early results are promising, there are still problems, such as not enough external validation, clinician trust issues, and worries about algorithmic bias. This brief communication will discuss about emerging new technologies, such as individualized antimicrobial risk assessments and real-time prediction systems that work with quick testing. For AI-CDSS to work well in emergency care, there needs to be collaboration between different fields, prospective trials, and fair model design. These tools could make a big difference in the fight against AMR around the world by cutting down on unnecessary antimicrobial use.