Yong Lorraine Pei Xian, Tung Joshua Yi Min, Cheung Nicole Mun Teng, Lee Zi Yao, Ng Ee Yang, Ng Alexander Jet Yue, Lim Clement Kee Woon, Boon Yuru, Lim Daniel Yan Zheng, Sng Gerald Gui Ren, Tang Jonathan Zhe Ying
Urgent Care Centre, Alexandra Hospital, Singapore, Singapore.
Emergency Medicine Department, National University Hospital, 5 Lower Kent Ridge Road, Singapore, 119074, Singapore, 65 67725000.
J Med Internet Res. 2025 Aug 22;27:e73121. doi: 10.2196/73121.
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 underexplored. This viewpoint aims to provide perspectives on AI applications in emergency toxicology by highlighting key advancements, challenges, and future directions. While AI has demonstrated significant potential in improving toxicological predictions through various applications, 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.
急诊毒理学是一个复杂的领域，需要迅速且精准地做出决策，以有效管理急性中毒情况。毒物暴露往往不可预测，时间和资源的限制常常对传统诊断和治疗方法构成挑战。人工智能（AI）已成为急诊医学中的一项宝贵工具，具有提高诊断准确性、预测临床结果以及改善临床决策支持系统的潜力。尽管人工智能在医学领域的关注度日益增加，但其在急诊毒理学中的应用仍未得到充分探索。本文观点旨在通过强调关键进展、挑战和未来方向，提供关于人工智能在急诊毒理学中应用的见解。虽然人工智能通过各种应用在改善毒理学预测方面已展现出巨大潜力，但数据质量、监管问题和实施障碍等挑战仍是其应用的障碍。需要进一步的研究、监管框架和整合策略，以确保在临床实践中有效且符合伦理地实施。
