Global trends in sepsis and artificial intelligence studies in intensive care units: Bibliometric analysis with Biblioshiny

Abstract

Objective: The aims of the study was to identify and visualize studies conducted between 2006 and 2025 in the fields of sepsis and artificial intelligence in intensive care units, with the aim of revealing trends in this area. Materials and Methods: The data were obtained from the Web of Science Core Collection database on May 2, 2025. Performance analysis, visualization, mapping, and bibliometric analyses were performed using the R software program Biblioshiny interface. For bibliometric data, a search was conducted in the WoS database using the keywords “intensive care” OR “ICU” OR “intensive care unit” OR “ICUs” AND “artificial intelligence” OR “machine learning” OR “deep learning” AND ‘Sepsis’ OR “sepsis prediction” in all files. The analysis of the study was conducted using 1,072 publication data. Results: The study found that the average annual number of articles produced in intensive care units in the fields of sepsis and artificial intelligence obtained from the WoS database was 2.76, with an annual growth rate of 27.63. A total of 1,072 articles were produced in 371 journals between 2006 and 2025. A total of 1,531 keywords were used. The average number of citations per publication was 18.13. It was observed that authors used 2,255 keywords across all publications, 7,015 authors were involved in these publications, only 6 articles had a single author, the average number of co-authors per article was 8.95, and the international co-authorship rate was 21.64%. Conclusions: The results of the bibliometric analysis showed that studies in this field are extremely recent. Studies conducted between 2006 and 2025 on sepsis and artificial intelligence in intensive care units have been included in the literature.

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