A bibliometric analysis of clinical studies on artificial intelligence in emergency medicine

Abstract

BACKGROUND: Interest in artificial intelligence (AI) and machine learning (ML) has grown rapidly in recent years due to the success of modern algorithms across various domains. Emergency departments are fast-paced and resource-constrained environments where timely decision-making is critical. These characteristics make them ideal settings for the integration of AI technologies, which have shown potential to enhance diagnostic accuracy and optimize patient outcomes. This study aims to identify and characterize the scientific literature on AI and ML applications in emergency departments over the past decade. METHODS: A comprehensive search was conducted in the Web of Science database on June 20, 2024. Articles published between 2015 and 2024 were considered. The search was performed using the keywords "artificial intelligence" or "machine learning" in all fields, limited to the "emergency medicine" category. The analysis of the articles included descriptive data on primary publication characteristics, such as the number of authors, citations, country of origin of the coauthors, and journal names. Bibliometric indicators were analyzed to identify publication trends and research themes, cluster analyses of keywords, and thematic maps. RESULTS: A total of 321 articles were analyzed. The average number of citations per article was 10.04, and the annual growth rate was 37.87%. Most publications originated from the United States. Resuscitation, American Journal of Emergency Medicine, Injury-International Journal of the Care of the Injured, and Resuscitation Plus published 107 articles. In 2024, the trending topic of the articles was "health," while "care" was the most popular in the last 10 years. The top 5 niche themes were "medical," "digital transformation," "education," "database," and "emergency care systems." CONCLUSION: This bibliometric analysis highlights the growing role of AI in emergency medicine. The findings provide insight into current research directions and may help inform future investigations in this evolving field.

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