Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis

Abstract

Background: Interest in critical care-related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally.

Objective: The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.

Methods: A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.

Results: The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.

Conclusions: This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.

Keywords: artificial intelligence; bibliometric analysis; intensive care medicine; machine learning; sepsis.

Figure 1

Machine learning is a branch of artificial intelligence encompassing two major approaches: supervised and unsupervised learning. Shown under each branch are algorithm types used in model development.

Figure 2

The annual and cumulative numbers of research articles on artificial intelligence in intensive care in Scopus from 1986 to 2022.

Figure 3

World map of the top 15 most productive countries based on publications on artificial intelligence in intensive care units.

Figure 4

Bibliometric map created based on coauthorship analysis between countries with network visualization mode. The QR code can be used to open this figure in VOSviewer.

Figure 5

Bibliometric map created based on author keywords co-occurrence with network visualization mode. The QR code can be used to open the figure in VOSviewer. Colors show clustering. Keywords in the same cluster are of the same color. The circle size increases with the number of times a keyword is used. ARDS: acute respiratory distress syndrome; ECG: electrocardiogram; EEG: electroencephalogram.

Figure 6

Bibliometric map created based on the co-occurrence of author keywords with overlay visualization mode. The color indicates the average publication year of the documents in which a keyword occurs. ARDS: acute respiratory distress syndrome; ECG: electrocardiogram; EEG: electroencephalogram.