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Artificial intelligence (AI) is revolutionizing emergency medicine by optimizing diagnosis, triage, and therapeutic decision-making. It enables rapid analysis of large volumes of data to assist health care professionals, without replacing them. Historically, AI’s breakthroughs came with deep learning in the 2010s, outperforming traditional methods through the use of deep neural networks. Supervised, unsupervised, and hybrid models process structured and unstructured data to predict key variables such as hospitalization or mortality. In pre-hospital care, AI improves the evaluation of emergency calls, optimizes resource allocation, and detects critical situations. In hospitals, it supports admission triage, diagnostics (of fractures and heart attacks, for instance), and patient routing, thereby streamlining patient flow. AI tools demonstrate increased sensitivity and specificity in interpreting ultrasounds and X-rays. However, integrating AI presents significant technical, ethical, security, and educational challenges. Ensuring system interoperability, data protection, and scientific validation of models is essential. Additionally, algorithmic biases and risks of errors remain major concerns, requiring ongoing human oversight. AI offers promising opportunities to enhance emergency medicine, but its deployment demands careful consideration of ethics, professional training, and liability in the event of errors. Maintaining human oversight over technology is crucial to ensure optimal and safe patient care. While AI enhances efficiency and decision-making, health care professionals must remain central to the process, ensuring a balance between technological advancements and human judgment for better patient outcomes.