Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia

To synthesize recent advances in intraoperative resuscitation for trauma surgery, including fluid composition, transfusion thresholds, and coagulopathy management, and to identify emerging directions that address persistent gaps in precision. Particular attention is given to prospective evidence, as forward-looking evaluations better capture real-world performance, workflow integration, and clinical impact, culminating in the integration of artificial intelligence and machine learning as the next stage of innovation.

Major trials over the past 5 years have refined evidence for balanced transfusion ratios, restrictive transfusion thresholds, and the transition from crystalloid to hemostatic and physiology-guided strategies. However, outcome variability underscores the need for individualized, data-driven resuscitation. Early prospective machine learning evaluations - along with the US Food and Drug Administration-cleared point-of-care systems such as APPRAISE-HRI - demonstrate that multimodal physiologic modeling can predict hemorrhage and fluid responsiveness as accurately as expert clinicians, while offering real-time monitoring and guidance.

The field is shifting from protocol-based to precision-guided trauma resuscitation. Future progress will rely on randomized-controlled-trial-validated, adaptive, human-in-the-loop artificial intelligence workflows that integrate continuous physiologic data with clinical judgment to enhance the timing, composition, and safety of intraoperative fluid and transfusion therapy.