Artificial intelligence (AI) has declared itself part of the future practice of radiology, particularly emergency radiology. Through advancements in machine learning, specifically deep learning architectures, FDA-approved AI tools are already available for critical tasks such as disease detection and report generation. These tools are already aiding radiologists in managing increasing imaging volumes and complexity under ever-increasing time constraints. Recent innovations, such as self-supervised learning and transformer models, have reduced the dependency on manually labeled data, enabling the creation of powerful AI models through contextual embeddings and attention mechanisms. These developments have ushered in the era of generative AI, capable of performing tasks beyond traditional classification, like generating detailed radiology reports based on minimal input data. The current offering of FDA-approved tools improves workflow efficiency, assists in critical finding detection, and can help provide a safety net for overlooked pathologies. AI applications are also available to assist in non-imaging tasks such as speech recognition and automated report generation, significantly improving reporting accuracy and efficiency. Although the long-term future is uncertain, the hope is that these tools will improve quality and efficiency, helping address both workforce shortages and the increasing demand for rapid, accurate interpretations in emergency settings. Ongoing collaboration between radiologists, industry partners, and regulatory bodies will be crucial in realizing AI’s full potential in emergency radiology.