JMIR Publications

ABSTRACT

Background and importance Emergency toxicology is a complex field requiring rapid and precise decision-making to manage acute poisonings effectively. Toxic exposures are often unpredictable, and the constraints of time and resources often challenge conventional diagnostic and treatment approaches. Artificial intelligence (AI) has emerged as a valuable tool in emergency medicine, offering the potential to enhance diagnostic accuracy, predict clinical outcomes and improve clinical decision support systems. Despite the increasing focus of AI in medicine, its applications in emergency toxicology are still under-explored. Objectives This narrative review aims to provide a comprehensive summary of AI applications in emergency toxicology by highlighting key advancements, challenges, as well as future directions. Design We conducted a narrative review of by examining current literature on AI applications in emergency toxicology. Results and conclusions AI has demonstrated significant potential in improving toxicological predictions through various applications. However, challenges such as data quality, regulatory concerns, and implementation barriers are still hurdles to its use. Further research, regulatory frameworks and integration strategies are needed to ensure effective and ethical implementation in clinical practice.