Artificial intelligence in the emergency department-- applications, perceptions and limitations: a narrative review

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into emergency department (ED) workflows to assist with time-sensitive decision-making, documentation, diagnostics, and education. AI encompasses multiple computational approaches, including traditional machine learning (ML), deep learning (DL), and large language models (LLMs). We aim to provide insight into the current integration of AI into the multiple clinical spheres of the emergency medicine. METHODS: This narrative review analyzes available literature on the implementation of ML-based predictive systems, DL-based image and signal interpretation tools, and LLM-driven documentation and clinical reasoning support within emergency medicine. RESULTS: A comprehensive literature search was conducted across PubMed and SCOPUS from its inception to October 30, 2025. Overall, 189 articles were found, among them 51 were included in the final review. ML and DL models demonstrated strong performance in electrocardiogram interpretation, radiographic triage, and sepsis prediction, in some cases outperforming traditional clinical scoring tools. LLMs showed promise in documentation support, discharge summary generation, triage assistance, and educational applications; however, concerns remain regarding generalizability and clinical reliability. AI-assisted triage systems improved prioritization and time-to-provider metrics in selected settings but require further validation. CONCLUSION: AI holds substantial potential to augment emergency care delivery. Nevertheless, issues of transparency, bias, accountability, and human oversight remain critical. Current evidence supports AI as a clinical support tool rather than a replacement for physician judgment.