Integrating AI Into Emergency Radiology: Promises, Pitfalls, and Practical Approaches

Emergency radiology operates in a high-acuity, time-sensitive environment where imaging is tightly integrated into real-time clinical decision-making. Growing imaging demand, increasing case complexity, and workforce constraints have intensified pressure on emergency radiologists. Artificial intelligence (AI) has emerged as a potential tool to support imaging prioritization, interpretation, and operational efficiency. However, to meaningfully advance care delivery, the role of AI must be considered beyond algorithm performance, including its implementation, reliability, and real-world clinical impact. In this narrative review, we examine the role of AI across the emergency radiology workflow through three lenses: current capabilities, limitations of the supporting evidence, and practical considerations for clinical implementation. We review applications spanning pre-image acquisition, image acquisition and reconstruction, computer-aided triage and detection, reporting, and follow-up, integrating published evidence with practical insights. Discrepancies between reported and real-world performance, the influence of human-AI interaction on clinical decision-making, and the potential for subtle errors and bias are also discussed. As national regulatory and local governance frameworks continue to evolve, including emerging challenges posed by large language models, gaps remain between reported and real-world AI performance. In emergency radiology, the true impact of AI will depend on how seamlessly and effectively these tools are integrated into existing clinical workflows. Local validation, ongoing performance monitoring, and multidisciplinary institutional oversight are essential to identify performance variability, mitigate biases, and support reliable use in a high-stakes clinical environment.