ABSTRACT
BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
ABSTRACT
BACKGROUND: Mushroom exposure is a global health issue. The manifestations of mushroom poisoning (MP) may vary. Some species have been reported as rhabdomyolytic, hallucinogenic, or gastrointestinal poisons. Critical or even fatal MPs are mostly attributable to Amanita phalloides, with the development of severe liver or renal failure. Myocardial injury and even cases mimicking ST-segment elevation myocardial infarction (STEMI) have been previously reported, while cardiac arrhythmia or cardiac arrest is not commonly seen. CASE SUMMARY: We report a 68-year-old woman with MP who suffered from delirium, seizure, long QT syndrome on electrocardiogram (ECG), severe cardiac arrhythmias of multiple origins, and cardiac arrest. She was intubated and put on blood perfusion. Her kidney and liver functions were intact; creatine kinase-MB was mildly elevated, and then fell within normal range during her hospital stay. We sent the mushrooms she left for translation elongation factor subunit 1α, ribosomal RNA gene sequence, and internal transcribed spacer sequence analyses. There were four kinds of mushrooms identified, two of which were found to be toxic. CONCLUSION: This is the first time that we found cardiac toxicity caused by Panaeolus subbalteatus and Conocybe lactea, which were believed to be toxic to the liver, kidney, and brain. We suggest that intensive monitoring and ECG follow-up are essential to diagnose prolonged QT interval and different forms of tachycardia in MP patients, even without the development of severe liver or renal failure. The mechanisms need to be further investigated and clarified based on animal experiments and molecular signal pathways.
