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.
The threat of multidrug-resistant bacteria has escalated rapidly, increasing the demand for accurate antibiotic susceptibility tests (ASTs). Traditional bacterial growth yield-based ASTs often take overnight to report, delaying the timely guidance of antibiotic use. Here, a fluorescent d-amino acid (FDAA) labeling-based AST (FaAST) is reported, which can quickly provide accurate minimum inhibitory concentrations (MICs). The FDAA-labeling signals that reflect the bacterial metabolic status underlie the flow cytometry-based strategy for MIC determination. Resistant bacteria show a reluctant decline in FDAA-labeling (inhibited metabolism) after treatment with the corresponding antibiotics, whereas susceptible bacteria demonstrate quick responses to low doses of drugs. The MICs are determined based on the changing trends in labeling. After testing 23 clinical isolates and laboratory strains of the most critical drug-resistant bacteria against a panel of representative antibiotics, FaAST shows a high susceptibility category with an accuracy of 98.13%. Moreover, FaAST can also make quick and accurate diagnosis against bronchoalveolar lavage fluids collected from hospital-acquired pneumonia patients, saving 2-4 days in guiding antibiotic use for this life-threatening infection. Thus, the speed, accuracy, and broad applicability of FaAST will be valuable in informing antibiotic decisions when treating critical infections caused by drug-resistant bacteria.
Via a fluorescent d-amino acid (FDAA) labeling-based antibiotic susceptibility test method, the minimum inhibitory concentrations of anaerobic bacteria were accurately determined in ∼5 h.
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