A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness

BACKGROUND: Artificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends. METHODS: A bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted. RESULTS: The number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. “Machine learning” was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by “risk”, “models”, and “mortality”. With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI. CONCLUSIONS: The application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial.gov, and the results of them are promising. KEYWORDS: Bibliometric analysis; artificial intelligence (AI); individualized diagnosis; critical care medicine; emergency department (ED)