@article{Granholm2026,
abstract = {Traumatic hemorrhage is a major cause of preventable death, where a critical challenge remains the dissociation between systemic hemodynamics and microcirculatory function. While macrocirculatory variables may be normalized during resuscitation, this does not necessarily ensure adequate tissue perfusion, leading to "hemodynamic coherence" loss and subsequent organ failure. Handheld vital microscopy (HVM) allows for bedside visualization of microcirculation, but clinical adoption is hindered by technical complexities in image acquisition and the time-consuming nature of manual analysis. Artificial intelligence (AI) is proposed as the missing translational link to make this monitoring clinically actionable. AI-based systems can enable automated video quality assessment and quantification of perfusion indices, reducing interobserver variability. Furthermore, AI could integrate microcirculatory data with macrocirculatory and biochemical streams to support individualized decisions regarding fluid and vasopressor therapy. However, several limitations persist. AI cannot compensate for poor primary data acquisition, and the clinical impact of microcirculatoryguided interventions remains unproven in interventional studies. Additionally, the integration of AI raises concerns regarding system robustness and cybersecurity. In conclusion, while AI-augmented monitoring is a promising step toward precise trauma resuscitation, it is not yet practice-changing and requires further validation through outcome-driven research.},
author = {Granholm, Fredrik},
doi = {10.32114/CCI.2026.9.2.37.41},
journal = {Critical Care Innovations},
number = {2},
pages = {37--41},
title = {{Patient microcirculation in trauma anesthesia: artificial intelligence as the missing translational link?}},
url = {https://www.irdim.net/cci/9(2)37-41.html},
volume = {9},
year = {2026}
}