JMIR Publications

ABSTRACT

Artificial Intelligence (AI), particularly large-scale language models (LLMs), such as ChatGPT, have rapidly evolved and are reshaping various fields, including clinical medicine. Emergency medicine stands to benefit from AI’s capacity for high-volume data processing, workflow optimization, and clinical decision support. However, important challenges exist, ranging from model “hallucinations” and data bias to questions of interpretability, liability, and ethical use in high-stake environments. This updated viewpoint provides a structured overview of AI’s current capabilities in emergency medicine, highlights real-world applications, and explores concerns regarding regulatory requirements, safety standards, and transparency (explainable AI). We discuss the potential risks and limitations of LLMs, including their performance in rare or atypical presentations common in the emergency department, and potential biases that could disproportionately affect vulnerable populations. We also address the regulatory landscape, particularly the liability for AI-driven decisions, and emphasize the need for clear guidelines and human oversight. Ultimately, AI holds enormous promise for improving patient care and resource management in emergency medicine; however, ensuring safety, fairness, and accountability remains vital.