Application, challenges and prospects of artificial intelligence in acute poisoning management
0.5847/wjem.j.1920-8642.2026.075
- Author:
Siwei He
1
Author Information
1. Wenzhou Medical University, Wenzhou 325035, China
- Publication Type:Review
- Keywords:
Acute poisoning;
Artificial intelligence;
Large language models;
Clinical decision support;
Telemedicine;
Prognostic assessment
- From:
World Journal of Emergency Medicine
2026;17(5):416-424
- CountryChina
- Language:English
-
Abstract:
BACKGROUND Acute poisoning remains a major global public health challenge; its management is often limited by diagnostic uncertainty, narrow therapeutic windows, mixed or unknown exposures, and uneven access to toxicology expertise. Although the use of artificial intelligence (AI) in medicine has been increasingly explored, its role in acute poisoning management warrants a focused review.
METHODS We conducted a narrative review of publications indexed in PubMed, Web of Science, and Wanfang Data from January 1, 2021 to March 1, 2026. Eligible publications addressed AI applications in acute poisoning. The included studies were driven by machine learning (ML) and deep learning (DL) architectures (e.g., artificial neural networks [ANNs]), employed technical modalities such as natural language processing (NLP), computer vision (CV), or large language models (LLMs), and utilized clinical decision support systems (CDSSs) or geographic information system (GIS)-assisted tools. Studies that incorporated explainable AI (XAI) methodologies were also included.
RESULTS Current evidence indicates that AI has been used for medical history acquisition, toxin identification, individualized treatment support, complication warning, prognostic assessments, remote consultations, emergency responses, and public education. Most tools, however, remain at the model development, retrospective validation, laboratory testing, or small-scale feasibility evaluation stage. Representative quantitative findings include the identification of CV-based snakes with a species-level accuracy of 96.0% and a genus-level accuracy of 99.0%; additionally, in selected poisoning scenarios, extreme gradient boosting (XGBoost)-based prognostic models have yielded area under the curve (AUC) values above 0.907. On the basis of this review, a three-stage framework and a full-process perspective are proposed for clinically grounded AI development.
CONCLUSION AI has substantial potential across the acute poisoning care continuum. Future translation will require multicenter data, external validation, interpretability, workflow integration, and ethical and regulatory oversight.
