Background/Objectives: Ketamine offers effective pain relief with fewer side effects than traditional analgesics, making it a promising alternative for acute pain treatment. However, further research is needed to fully assess its role in perioperative care. This umbrella review aimed to compile the highest-quality evidence available regarding the application of ketamine in managing acute pain. Methods: A thorough search of the literature was carried out in PubMed, Scopus, and the Cochrane Library, including systematic reviews that focused on the application of ketamine in managing acute pain. The data extraction included the research type, analgesics used, number of studies and patients per review, pain types, scoring methods, ketamine doses, administration routes, and reporting guidelines. Results: Of the 807 records identified, 20 studies met the inclusion criteria. In accordance with the AMSTAR-2 evaluation, most of the systematic reviews were rated as critically low quality. Intravenous ketamine administered during the perioperative period was found to reduce the pain intensity of acute pain within 15–30 and 60 min following treatment, and decrease postoperative opioid consumption by 14–50% at both 24 and 48 h after surgery. Conclusions: Evidence shows that intravenous ketamine reduces the pain intensity, postoperative opioid use, and the risk of vomiting and nausea while improving analgesia, making it a valuable adjunct in perioperative pain management.

Background: Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs. This scoping review aimed to consolidate and evaluate the existing evidence for the application of AI in managing respiratory failure among patients necessitating MV. Methods: The literature search was conducted in PubMed, Scopus, and the Cochrane Library. Studies investigating the utilization of AI in patients undergoing MV, including observational and randomized controlled trials, were selected. Results: Overall, 152 articles were screened, and 37 were included in the analysis. We categorized the goals of AI in the included studies into the following groups: (1) prediction of requirement in MV; (2) prediction of outcomes in MV; (3) prediction of weaning from MV; (4) prediction of hypoxemia after extubation; (5) prediction models for MV–associated severe acute kidney injury; (6) identification of long-term outcomes after prolonged MV; (7) prediction of survival. Conclusions: AI has been studied in a wide variety of patients with respiratory failure requiring MV. Common applications of AI in MV included the assessment of the performance of ML for mortality prediction in patients with respiratory failure, prediction and identification of the most appropriate time for extubation, detection of patient-ventilator asynchrony, ineffective expiration, and the prediction of the severity of the respiratory failure.

Meiram Tungushpayev and Dmitriy Viderman. Korean J Pain 2025;38:449-71. https://doi.org/10.3344/kjp.25019