Clinical Applications, Opportunities, and Implementation Challenges of AI in Emergency Medicine: A Narrative Review

EDs operate in high-acuity, time-sensitive, and resource-constrained environments where crowding, diagnostic uncertainty, boarding, variable patient acuity, and increasing documentation demands place substantial pressure on clinicians and health systems. AI, including machine learning, deep learning, natural language processing, computer vision, predictive analytics, and large language models (LLMs), is increasingly being investigated as a supportive technology for emergency care. This narrative review summarizes current and emerging applications of AI in emergency medicine, with emphasis on triage and risk stratification, diagnostic decision support, emergency imaging, patient-flow optimization, LLMs, and implementation governance. The strongest near-term applications are likely to be those that address clearly defined clinical or operational tasks, use available electronic health record or imaging data, integrate into existing workflows, and undergo prospective validation in the intended setting. AI may support earlier recognition of high-risk patients, prioritization of critical imaging findings, prediction of hospital admission or deterioration, drafting and summarization of clinical documentation, and operational planning. However, evidence remains uneven across use cases. Important barriers include limited external validation, dataset shift, bias, false alarms, alert fatigue, lack of interpretability, privacy risks, uncertain liability, regulatory complexity, and difficulty demonstrating patient-centered outcome improvement. LLMs introduce additional risks, including hallucination, outdated information, unsafe recommendations, and privacy concerns, and should not be used as autonomous decision-makers in emergency care. Responsible implementation requires human oversight, local validation, post-deployment monitoring, equity assessment, governance, and alignment with emergency clinician workflows. AI should be viewed as a tool to augment, not replace, emergency physicians and multidisciplinary ED teams.