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.

In gravity-free tapered capillaries, wetting ganglia spontaneously migrate toward the narrow end, whereas non-wetting ganglia move toward the wider end due to capillary-force imbalance. Their motion is governed by competing inertial and viscous effects, with inertia dominating initially and viscosity becoming increasingly important as motion progresses. This study develops a reduced-order model to capture ganglion dynamics and a Volume of Fluid-based computational fluid dynamics model for verification and validation. An inverse physics-informed neural network calibrates uncertain model parameters, including those associated with dynamic contact-angle correlations, against CFD predictions. The calibrated model is solved independently using a fourth- and fifth-order Runge–Kutta method and a PINN-based solver. Model accuracy is evaluated against CFD results using normalized root mean square error, root mean square error, mean absolute error, and the coefficient of determination (R²). Excellent agreement demonstrates the model's predictive capability and computational simplicity. A parametric study further elucidates the underlying physical mechanisms