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.

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

“…The large volumes of rapidly captured digital data from earlier sources, called Big Data, were too large for standard analytical techniques; hence, an alternative analytical method, capable of analyzing, integrating, and storing all the available information was needed [ 3 , 14 , 15 ]. As in other scientific areas, AI application helped achieve this goal.…”

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis

“…In bibliometrics, network charts are frequently used to report the collaborations and cooccurrences involving the most active international entities across countries, research institutes, departments, authors, and keywords. [3,28,[46][47][48][49][50] However, these charts often suffer from unclear labeling and relationships between members. To address these limitations, this study will demonstrate the use of 5 CNCs in AI for critical care.…”

“…The figure illustrates 3 types of network charts used in bibliometric analyses: clusters [3,28,[46][47][48][49][50]52] (clusters are randomly scattered); circles [53][54][55][56][57][58] (members are grouped by cluster and bubble size), and chords [43][44][45]59] (similar to the circle chart with significant relationships between members). Readers will find that the CNC with a dashboard-type representation has a meaningful and clear interface.…”

“…We found that diversity in research partners, a high proportion of foreign postgraduates or visiting scholars, and adequate research funding were all factors contributing to improved international collaboration. [3] A flexible and stable research policy is also crucial for ensuring the sustainability of international collaboration. [79] According to the keywords plus in WoS, the top domains of disease covered in critical care medicine-related AI research were mortality, model, and intensive care unit.…”

“…[1] Machine learning (ML), natural language processing, and computer vision, which involve the ability to visualize and recognize objects, are some of the widely employed AI technologies. [2] While AI has found diverse applications in the medical sector, such as molecular biology, bioinformatics, and medical imaging, [3][4][5] it has also been utilized for population health management, personalized diagnosis and treatment, patient monitoring,…”